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GLM-5.3: specs, benchmarks, pricing & API options — now served unlimited

Aug 14, 2026 - Last update: Aug 30, 2026

Update, August 30, 2026: the review is over — GLM-5.3 is live in our Frontier Pool as glm-5.3. Z.ai published the open weights on August 28 (zai-org/GLM-5.3, under the custom GLM-5.3 License — commercial use allowed; a security-review clause applies only to Model-as-a-Service operators above US$10B revenue), the licensing gate we describe below lifted, and the pool’s GLM slot was upgraded in place: every Frontier subscriber gets GLM-5.3 at the same flat price, and glm-5.2 requests keep working until September 30, 2026. Setup on the model page. The analysis below is as written on launch day.

GLM-5.3 (API id glm-5.3) is Z.ai’s (Zhipu AI) new coding and agentic model, released today, August 14, 2026, under the tagline “Built to Code. Ready for Cyber Defense.” The architecture story is unusual and worth being precise about: GLM-5.3 keeps the same 743B-parameter base model as GLM 5.2 — every reported gain comes from scaled-up post-training alone. On Z.ai’s own benchmark suite that post-training buys a lot: it calls GLM-5.3 the strongest open-weights coding model it has measured, and reports a cyber-security capability that grew faster than the company anticipated.

And the question this blog exists to answer: GLM-5.3 was officially under review for our pools as of launch day — and went live on August 30 (see the update above). We already serve GLM 5.2 in the Frontier Pool, so 5.3 enters the pipeline as the natural upgrade candidate for that slot. What gates the decision is not quality signals — it’s that the model is API-only today: open weights are promised roughly two weeks out, after Z.ai completes its own safety evaluation. Live status is always on our models-under-review page.

GLM-5.3 specs


Base model	Same 743B base as GLM 5.2 — not a new pretrain; gains from extended post-training
Context window	Z.ai advertises a 1M-token variant (`glm-5.3[1m]`, with context compaction); the standard-API spec is not yet published
Reasoning	Effort levels `low` / `high` / `max` — default `max`; thinking cannot be disabled on the direct API
Open weights	Published August 28, 2026 — zai-org/GLM-5.3 (fp8, 141 shards); at launch they were promised ~2 weeks out
License	GLM-5.3 License (custom): commercial use with attribution; Model-as-a-Service operators above US$10B revenue over 12 months must pass Z.ai’s security review. Not MIT — GLM 5.2’s terms did not carry over
List price (API)	$1.40 in / $4.40 out per 1M, cached input $0.26 — unchanged from GLM 5.2
Availability	First-party API access from Z.ai; open weights since August 28; served unlimited on CheapestInference’s Frontier Pool since August 30 — works with Claude Code, OpenCode, Cline and Codex via compatible endpoints

GLM-5.3 benchmarks: what post-training bought

All numbers below are Z.ai’s own reported results — vendor-run, not yet independently reproduced, and the Artificial Analysis index hasn’t rated GLM-5.3 yet. With that caveat on the table, the GLM 5.2 → GLM-5.3 deltas are the story, because the base model is identical:

Benchmark	GLM 5.2	GLM-5.3	Δ
Terminal-Bench 2.1	81.0	88.2	+9%
Terminal-Bench 3.0	4.6	28.3	+515%
DeepSWE v1.1	46.2	66.9	+45%
SWE-Marathon v1.1	19.4	42.5	+119%
FrontierSWE	67.5	78.1	+16%
NL2Repo	48.9	58.0	+19%
Toolathlon Verified	59.9	73.0	+22%
AutomationBench v1.0.6	26.2	48.2	+84%
CyberGym	77.2	84.5	+9%

Two readings. The charitable one: the biggest jumps land on the newest, hardest agentic benchmarks (Terminal-Bench 3.0, SWE-Marathon) — exactly where post-training on agent trajectories should show up, and exactly the workloads coding agents run all day. The skeptical one: several of these benchmarks are new or Z.ai-adjacent, and until independent runs land, “strongest open-weights coding model” is a claim, not a fact. Both readings can wait two weeks — the open-weights release is when independent verification becomes possible.

The cyber-defense angle — and why the weights are two weeks out

The unusual part of this launch is that Z.ai leads with cyber security as a first-class capability, not a footnote. It reports GLM-5.3 at 84.5 on CyberGym — above its figures for Claude Mythos 5 (83.8) and GPT-5.6 Sol (83.6) — and says the model found thousands of real vulnerabilities across open-source projects during training. Z.ai’s framing is defensive: vulnerability detection at scale.

That capability is also the stated reason the weights aren’t out yet. Rather than shipping weights on day one — as it did with GLM 5.2 — Z.ai is running a staged release: API first, then open weights roughly two weeks after launch, once its own safety evaluation and hardening work is complete. Whatever you think of the trade-off, it’s a more deliberate open-weights process than the ecosystem norm, and it puts a concrete clock on the one thing our review is waiting for.

GLM-5.3 vs GLM 5.2 — the model we serve today

	GLM-5.3	GLM 5.2
Base	743B (same base)	743B
What’s new	Scaled post-training: agentic coding, tool use, cyber	—
Context	Z.ai advertises a 1M-token variant (`glm-5.3[1m]`, with context compaction); the standard-API spec is not yet published	1M per the model card
Reasoning	Effort `low` / `high` / `max`, thinking always on	Standard GLM 5.2 semantics
Open weights	Published August 28 (GLM-5.3 License)	Published (MIT)
List price (per 1M)	$1.40 in / $4.40 out	$1.40 in / $4.40 out
Status here	Live in the Frontier Pool since August 30	Retired August 30 — migration

Because the base is unchanged, this isn’t a “new model vs old model” decision so much as a post-training upgrade — the same shape as DeepSeek’s V4-Flash-0731 build, which we upgraded in place in the Core Pool within days of release. If GLM-5.3’s weights land with a usable license and it passes our quality evaluation on real coding and agent workloads, the natural outcome is the same: the Frontier Pool’s GLM slot upgrades, and every existing subscription simply gets the better model.

So the honest status board:

Quality — vendor numbers are strong; our own evaluation on real agent workloads (both OpenAI and Anthropic endpoints, tool calling included): ✅ passed.
Fit — a post-training upgrade of a model already serving Frontier Pool workloads: as clean as fit gets.
Licensing — ✅ open weights published August 28 under the GLM-5.3 License.

Outcome (August 30): live. The upgrade is recorded in the changelog; the models-under-review page is where the next candidate will show up.

Common questions

Is there an unlimited GLM-5.3 API? Yes — since August 30, 2026 CheapestInference serves GLM-5.3 in the Frontier Pool on flat-rate time-block subscriptions from $71/mo: no token caps during your reserved hours, model id glm-5.3, OpenAI- and Anthropic-compatible.

Does GLM-5.3 have open weights? Yes, since August 28, 2026 — zai-org/GLM-5.3 on Hugging Face, under Z.ai’s custom GLM-5.3 License: commercial use is allowed with attribution, and only Model-as-a-Service operators above US$10B aggregate revenue over 12 months must pass Z.ai’s security review first. GLM 5.2 was MIT; the terms did not carry over.

How much does the GLM-5.3 API cost? Per token, Z.ai lists $1.40 in / $4.40 out per 1M (cached input $0.26) — the same as GLM 5.2. On CheapestInference it is a flat monthly fee: from $71/mo for a daily 8-hour block, unlimited tokens.

What is the difference between GLM-5.3 and GLM 5.2? Same 743B base model — GLM-5.3 is extended post-training on top of it, targeting agentic coding, tool use, and cyber-security workloads. Z.ai reports large gains on agentic benchmarks (SWE-Marathon 19.4 → 42.5, Terminal-Bench 3.0 4.6 → 28.3); all numbers are vendor-run so far.

CheapestInference serves Kimi K3 and Qwen3.8 Max (Flagship Pool), GLM 5.3 and MiniMax M3 (Frontier Pool) and DeepSeek V4.1 Flash and MiMo V2.6 Flash (Core Pool) through one OpenAI- and Anthropic-compatible API on unlimited time-block subscriptions. See the pools or get started.

Qwen3.8 Max: specs, benchmarks, pricing & API options — now live in the Flagship Pool

Aug 4, 2026

Qwen3.8 Max (also written “Qwen 3.8 Max”; API id qwen3.8-max) is Alibaba’s flagship model, announced July 19: a 2.4-trillion-parameter system with a 1M-token context window, scoring 58 on the independent Artificial Analysis Intelligence Index (v4.1.1) — top-five territory, two points behind Kimi K3 (60), the current open-weights ceiling. As we chart below, that score at Qwen’s list price lands it on the price-vs-intelligence Pareto frontier — and knocks Claude Sonnet 5 off it.

Update, August 14, 2026: the review is over — Qwen3.8 Max is live in our Flagship Pool. Alibaba shipped the open-weight variant on August 13, the licensing gate we describe below lifted, and every Flagship subscriber can now call it with model id qwen3.8-max — unlimited, flat-rate, from $199/mo, next to Kimi K3. Setup, specs and examples: Qwen3.8 Max API — pricing, access & subscription.

curl https://api.cheapestinference.com/v1/chat/completions \
  -H "Authorization: Bearer YOUR_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{"model": "qwen3.8-max", "messages": [{"role": "user", "content": "Hello"}]}'

The analysis below is the review that got it there, kept as published (August 4) with the status lines updated.

Qwen3.8 Max specs and benchmarks


Architecture	~2.4T total parameters (MoE details unpublished)
Context window	1M tokens
Open weights	Released August 13, 2026 — Qwen3.8-2.4T-A95B, the open variant of the Max
Vision	Yes — image input
List price (API)	$2 input / $6 output per 1M tokens (cached input from $0.25)

The benchmark picture: 58 on the Artificial Analysis index (v4.1.1) puts Qwen3.8 Max above every previous Qwen release and within two points of Kimi K3 (60). Independent benchmarking is ongoing; active-parameter counts and MoE configuration haven’t been published, so per-token compute cost can’t be derived yet.

Qwen3.8 Max vs Kimi K3

The head-to-head everyone is asking for — the two highest-scoring models in the open(-ing) ecosystem, and on paper they’re complements rather than rivals:

	Qwen3.8 Max	Kimi K3
AA Intelligence Index	58	60
Parameters	~2.4T (MoE, config unpublished)	~2.8T MoE
Context window	1M tokens	1M tokens
List price (in / out per 1M)	$2.00 / $6.00	$3.00 / $15.00
Open weights	Published August 13, 2026 (2.4T-A95B)	Published July 27, 2026
Status here	Live in the Flagship Pool since August 14	Live in the Flagship Pool

K3 holds the intelligence crown and the agentic-coding pedigree; Qwen3.8 Max answers with 2.5× cheaper output at two index points’ distance, plus standard sampling controls. One pool serving both covers the two profiles that matter — peak agentic reasoning and tunable long-context breadth — which is exactly what the Flagship Pool now does.

Qwen3.8 Max on the price-vs-intelligence Pareto frontier

A model is on the frontier when nothing tracked is both smarter and cheaper. At index 58 for a $6.00/1M list output price, Qwen3.8 Max steps onto the frontier — five points above Claude Sonnet 5 at 40% lower list price — and pushes Sonnet 5 off it:

Served in a CheapestInference poolUnder reviewReference frontier modelsPareto frontier

Four of the five models on that frontier — V4-Flash-0731, GLM 5.2, Qwen3.8 Max and Kimi K3 — are served here on flat rate. The monthly-updated, full-field version of this chart (with cost-per-task data and edition history) lives in our LLM Pareto Frontier report.

What gated the decision — and how it resolved

When this review was published (August 4), Qwen3.8 Max had no open weights and no open license. Qwen’s open releases (3.5, 3.6) shipped under Apache 2.0; its Max tier had historically stayed closed — a tension the community debated openly since the preview shipped. Our review status was explicit that licensing, not quality, was the gate.

On August 13 Alibaba resolved it: Qwen3.8-2.4T-A95B — the open-weight variant of the Max — shipped on Hugging Face and ModelScope, followed on August 14 by the dense Qwen3.8-27B under Apache 2.0. The final scorecard:

Quality — reviewed on real agent workloads through both our OpenAI and Anthropic endpoints, including tool calling: ✅ passed.
Fit — a second flagship-class model with a different profile (hybrid reasoning, standard sampling, 1M context, vision) next to Kimi K3: ✅ strong.
Licensing — ✅ open weights published August 13.

Result: live in the Flagship Pool on August 14 — the pipeline’s fastest gate-to-launch turnaround so far. The models-under-review board and the changelog reflect it.

Common questions

Is there an unlimited Qwen3.8 Max API? Yes — live since August 14, 2026: the CheapestInference Flagship Pool serves Qwen3.8 Max with no token caps during your reserved hours, from $199/month for a daily 8-hour block, on the same subscription as Kimi K3. Model id qwen3.8-max; setup on the model page. Seats are very limited.

Does Qwen3.8 Max have open weights? Yes, as of August 13, 2026: Alibaba published Qwen3.8-2.4T-A95B, the open-weight variant of the Max, on Hugging Face and ModelScope — and the dense Qwen3.8-27B followed on August 14 under Apache 2.0.

How much does the Qwen3.8 Max API cost? Per token, list price is $2.00 per 1M input tokens and $6.00 per 1M output, with cached input from $0.25 — a 100M-token month lands around $200–600 depending on cache-hit rate and output mix. The credit-based subscription plans we analyzed in Qwen coding plans, explained cap usage per 5-hour and 7-day windows. For heavy use, our flat-rate unlimited route starts at $199/month.

What is the context window of Qwen3.8 Max? 1M tokens, per Alibaba’s published spec for the model.

DeepSeek V4-Flash-0731: what changed, and how to use it over the API

Aug 1, 2026 - Last update: Sep 10, 2026

Update — September 10, 2026: DeepSeek V4.1 Flash replaced this build in the Core Pool on 2026-09-10, served as deepseek-v4.1-flash — a new generation (552B MoE, causal encoder–decoder, native vision, MIT weights), not another retrain. The old id deepseek-v4-flash keeps working until October 10, 2026, then returns an invalid-model error; migration is one line. Full analysis: DeepSeek V4.1 Flash: what changed, and how to use it over the API. The post below is kept as published on August 1, 2026.

On July 31, DeepSeek shipped V4-Flash-0731 — not a bigger model, a retrained one. Same 284B-total / 13B-active architecture, same 1M-token context, same price bracket — but re-post-trained for agent work, and the reported jump is unusual: DeepSeek says the new Flash now beats its own larger V4-Pro-Preview on every one of the nine agent benchmarks it publishes.

If you use the Core Pool, there is nothing to migrate: the model id is still deepseek-v4-flash, and requests already serve the new build. Unlimited, flat-rate access starts from $22/mo ($18.70/mo billed annually) — live pricing on /pools.

The retrain, measured

Same architecture, new post-training. This is DeepSeek’s own reported before/after (vendor harness — no independent verification of these specific numbers yet):

V4-Flash-0731V4-Flash preview (the previous build, April 2026)

Full vendor-stated table for the 0731 build, next to the April preview it replaces:

Benchmark	Flash-0731	Flash preview (Apr)
Terminal-Bench 2.1	82.7	61.8
Cybergym	76.7	38.7
Toolathlon-Verified	70.3	49.7
DSBench-FullStack†	68.7	37.0
DSBench-Hard†	59.6	25.8
DeepSWE	54.4	7.3
NL2Repo	54.2	39.4
Agents’ Last Exam	25.2	15.8
AutomationBench Public	25.1	10.8

† DeepSeek-internal test sets; the rest are public benchmarks.

The DeepSWE number is the striking one: the retrain multiplied the preview’s score by seven without touching the architecture. And like the April preview, the 0731 checkpoint is a genuine open-weights release: the weights are published under MIT at deepseek-ai/DeepSeek-V4-Flash-0731 (304B parameters on the repo — the 284B base plus a draft module).

Where that lands among frontier models

The independent signal comes from Artificial Analysis, which measures Flash-0731 at 50 on its Intelligence Index — ten points above the previous Flash. Honest framing: it does not beat Claude Opus 5 (61) or GPT-5.6 Sol (59) — nothing near this price does. What it does is land within seven points of Kimi K3 (57, the highest open-weights score on the index) from the budget model of the field — with a reference per-token price of $0.28 per 1M output tokens, roughly 90× below Claude Opus 5’s $25:

Served in a CheapestInference poolReference frontier models

Same data as a table, with list output prices alongside for scale — each model at its best published configuration (max effort where the index reports one); the median across all models Artificial Analysis tracks is 17:

Model	AA Intelligence Index	List output $/1M	On CheapestInference
Claude Opus 5	61	$25.00	—
Claude Fable 5*	60	$50.00	—
GPT-5.6 Sol	59	$30.00	—
Kimi K3	57	$15.00	Flagship Pool
Claude Opus 4.8	56	$25.00	—
GLM 5.2	51	$4.40	Frontier Pool
DeepSeek V4-Flash-0731	50	$0.28	Core Pool
MiniMax M3	44	$1.20	Frontier Pool

* Yes, one point below Claude Opus 5, even though Anthropic positions Fable 5 above Opus in capability. The index scores what AA actually evaluates: Fable 5 is measured in its “Adaptive Reasoning, Max Effort, Opus 4.8 Fallback” serving configuration — the variant with Anthropic’s additional dual-use safeguards — while Opus 5 runs at plain max effort. Independent index, published configurations, taken as-is.

Four of the eight models on that chart are served here on flat-rate subscriptions — and the 0731 retrain just moved the cheapest pool of the three into frontier territory.

V4-Flash-0731 vs Claude Opus 4.8, GPT-5.6 Luna, and Gemini 3.6 Flash

Is DeepSeek V4-Flash-0731 better than Claude Opus? On raw capability, no — and we won’t pretend otherwise. The strongest competitor in DeepSeek’s own release table is Claude Opus 4.8, and Opus wins every one of the nine shared benchmarks. What the table actually shows is how little it wins by, against a model priced roughly 90× higher per output token at list:

Benchmark (DeepSeek’s release table, vendor-run)	V4-Flash-0731	Claude Opus 4.8
Terminal-Bench 2.1	82.7	85.0
Cybergym	76.7	83.1
Toolathlon-Verified	70.3	76.2
DSBench-FullStack†	68.7	71.6
DSBench-Hard†	59.6	71.7
DeepSWE	54.4	58.0
NL2Repo	54.2	69.7
Agents’ Last Exam	25.2	25.7
AutomationBench Public	25.1	27.2
Reference output price, per 1M tokens	$0.28	$25.00

On Agents’ Last Exam the gap is half a point — effective parity. On Terminal-Bench 2.1 it is 2.3 points. NL2Repo and the (internal) DSBench-Hard are where the distance stays wide.

Against its actual price peers, the independent picture flips. Per Artificial Analysis: Flash-0731 sits one point behind GPT-5.6 Luna (50 vs 51 at max effort) with a cost per task roughly 60% lower — even after OpenAI’s price cut — ties Gemini 3.6 Flash (50), and lands on AA’s Pareto frontier for Intelligence vs Cost per Task: at this intelligence level, nothing tracked is cheaper per task.

Here is that frontier drawn out — intelligence against list output price. A model is on the frontier when nothing tracked is both smarter and cheaper; everything below-right of the line pays more for less. The 0731 retrain moved Flash onto it, and pushed MiniMax M3 off:

Served in a CheapestInference poolReference frontier modelsPareto frontier

Three of the five models on that frontier — Flash-0731, GLM 5.2 and Kimi K3 — are served here on flat rate. This chart is a snapshot of the frontier’s cheap end; the monthly-updated, full-field version (with cost-per-task data and edition history) lives in our LLM Pareto Frontier report.

And on a flat-rate subscription the per-token column stops mattering altogether: a Core Pool block is the same price whether your agent burns one million tokens or one billion.

Call it

curl:

curl https://api.cheapestinference.com/v1/chat/completions \
  -H "Authorization: Bearer YOUR_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{"model": "deepseek-v4-flash", "messages": [{"role": "user", "content": "Hello"}]}'

OpenAI SDK (Python):

from openai import OpenAI

client = OpenAI(
    base_url="https://api.cheapestinference.com/v1",
    api_key="sk-...",  # your subscriber key
)

response = client.chat.completions.create(
    model="deepseek-v4-flash",
    messages=[{"role": "user", "content": "Fix the failing test in this repo..."}],
)
print(response.choices[0].message.content)

No account yet? Register, subscribe to the Core Pool, and mint a key at cheapestinference.com/keys.

Run it in Claude Code

The API also speaks the Anthropic Messages format, so a retrained agent model drops straight into the most popular coding agent:

export ANTHROPIC_BASE_URL="https://api.cheapestinference.com/anthropic"
export ANTHROPIC_AUTH_TOKEN="sk-..."   # your subscriber key
export ANTHROPIC_MODEL="deepseek-v4-flash"
export ANTHROPIC_SMALL_FAST_MODEL="deepseek-v4-flash"

Start claude as usual — every request runs on Flash-0731 with no per-token meter. The same key works in Cline, Roo Code, Continue, and anything that accepts a custom OpenAI base URL (setup guides).

Why this release matters for flat-rate users

Agent workloads are exactly where per-token bills explode: an agent re-sends its growing context on every tool call, and iteration count — not task value — drives the invoice. A model that is suddenly much better at agent work makes that math worse per-token and better flat-rate: more capable loops, same fixed monthly price. We ran the full per-token vs. flat break-even math in Unlimited DeepSeek: what a flat monthly subscription changes — every row of that table just got more favorable, because the same subscription now serves a stronger model.

The trade-offs, as always: usage is unlimited in tokens during your reserved 8-hour blocks, each subscription runs one request at a time (fair use), and outside your blocks the key doesn’t serve. Full details: DeepSeek V4 Flash API docs · Plans & Limits.

Check live Core Pool availability →

CheapestInference serves Kimi K3 (Flagship Pool), GLM 5.3 and MiniMax M3 (Frontier Pool) and DeepSeek V4.1 Flash and MiMo V2.6 Flash (Core Pool) through one OpenAI- and Anthropic-compatible API on unlimited time-block subscriptions. See the pools or get started.

How to use the Kimi K3 API: key, snippets, and unlimited access

Jul 27, 2026 - Last update: Sep 22, 2026

Kimi K3 is live on CheapestInference: Moonshot’s flagship — scoring 44 on the Artificial Analysis Intelligence Index (v4.3.2), 9 points off the top score (53) and above Claude Opus 4.8, Sonnet 5 and GPT-5.5 — served through an OpenAI- and Anthropic-compatible API with unlimited usage at a flat monthly price, from $199/mo (live availability — seats are very limited). This is the practical guide: get access, call it, wire it into your coding agent.

1. Get access

Three realistic routes to K3 over an API today:

Per-token, from Moonshot — $3.00 per 1M input tokens ($0.30 cached) and $15.00 per 1M output. Ideal for evaluation and light use; expensive fast for agent workloads.
Kimi memberships — Moonshot’s own plans bundle K3 access with request quotas per 5-hour and weekly windows (new signups have been intermittently paused since launch).
Unlimited time-block subscription (this guide) — reserve one or more daily 8-hour blocks on the Flagship Pool and use K3 with no token caps during your hours. From $199/mo per block; all three blocks = 24/7.

For the subscription route: create an account, subscribe to the Flagship Pool, and mint an API key at cheapestinference.com/keys. Model id: kimi-k3.

2. Call Kimi K3

curl:

curl https://api.cheapestinference.com/v1/chat/completions \
  -H "Authorization: Bearer YOUR_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{"model": "kimi-k3", "messages": [{"role": "user", "content": "Hello"}]}'

OpenAI SDK (Python):

from openai import OpenAI

client = OpenAI(
    base_url="https://api.cheapestinference.com/v1",
    api_key="sk-...",  # your subscriber key
)

response = client.chat.completions.create(
    model="kimi-k3",
    messages=[{"role": "user", "content": "Refactor this function..."}],
)
print(response.choices[0].message.content)

K3 is a reasoning model, but reasoning is off by default — you get fast, direct answers. Turn it on per request with "thinking": {"type": "enabled"} or reasoning_effort (low / high / max — K3’s deepest tier). With reasoning on, give it a generous max_tokens so long answers don’t truncate mid-thought, and stream (stream: true) for responsive UIs.

3. Run it in Claude Code

The API also speaks the Anthropic Messages format, so Claude Code works out of the box:

export ANTHROPIC_BASE_URL="https://api.cheapestinference.com/anthropic"
export ANTHROPIC_AUTH_TOKEN="sk-..."   # your subscriber key
export ANTHROPIC_MODEL="kimi-k3"
export ANTHROPIC_SMALL_FAST_MODEL="kimi-k3"

Start claude as usual — every request now runs on K3 with no per-token meter. The same key works in Cline, Roo Code, Continue, and any client that accepts a custom OpenAI base URL (setup guides).

What it costs, honestly

At list price, a coding agent that burns 100M tokens a month on K3 costs roughly $200–400 per-token, depending on your cache-hit and output mix — and heavy agentic users go far beyond that. A Flagship block at $199/mo covers your working day; all three blocks cover 24/7. The trade-offs: during your reserved hours usage is truly unlimited, each subscription runs one request at a time (fair use), and outside your blocks the key doesn’t serve. Full pricing detail: Plans & Limits · Kimi K3 API docs.

Seats on the Flagship Pool are very limited — when a block sells out it’s gone until someone leaves. Check live availability →

Kimi K3: specs, benchmarks, and our day-one plan to serve it

Jul 22, 2026 - Last update: Jul 28, 2026

Kimi K3 is Moonshot AI’s new flagship, announced on July 16 after a leaked promotion page on Moonshot’s own platform tipped the release a day early. The headline is simple: on the independent Artificial Analysis Intelligence Index it scores 57 — above Claude Opus 4.8 (56), making it the first open-weight model to outscore a Claude Opus-class model on that index.

And to answer the question this blog exists to answer: yes, we will serve it. We have already secured the capacity to run a model of this size. K3 goes live on CheapestInference the moment two things are true: the weights are actually published, and the license terms permit commercial serving. Nothing else is in the way.

Update — July 2026: Kimi K3 is now live. The weights shipped on schedule (July 27), and K3 is served in the new Flagship Pool with unlimited usage at a flat monthly price — very limited seats. How to use the Kimi K3 API → · Subscribe →

July 28: with Claude Opus 5 (61) and GPT-5.6 Sol (59) landing the same month, K3’s 57 puts the open-vs-closed gap at 4 index points — per Artificial Analysis, the narrowest since the GLM-5 release in February. Only two labs score higher. Our Pareto Frontier report now charts K3 — straight onto the frontier.

What Moonshot announced


Architecture	Mixture-of-Experts, ~2.8T total parameters
Context window	1M tokens
Input	Text, image, and video
Variants at launch	K3 Max (chat and agent tasks) · K3 Swarm Max (large-scale parallel processing)
Available today	Moonshot’s API, Kimi Code, and the Kimi app
Open weights	Published July 27, 2026 — Hugging Face, Kimi K3 License
List price (API)	$3 input / $15 output per 1M tokens

Coverage from the launch day: TechCrunch on the Opus gap closing, Fortune on Chinese AI entering Fable-level territory, and Simon Willison’s notes for a practitioner’s first look.

When is the Kimi K3 release date?

K3 was announced on July 16, 2026, usable from day one through Moonshot’s own API, Kimi Code, and the Kimi app. The open weights were published on July 27, 2026, on schedule, on Hugging Face under Moonshot’s own Kimi K3 License — and K3 went live on CheapestInference’s Flagship Pool the same week.

The numbers so far

Artificial Analysis Intelligence Index: 57. For scale: Claude Opus 4.8 scores 56, and the best open-weight model until now — GLM 5.2 — scores 51: the open ceiling jumped six points in one release. Only three closed models score higher — Claude Opus 5 (61), Claude Fable 5 (60) and GPT-5.6 Sol (59). Where every model sits on price-vs-intelligence is our Pareto Frontier report, whose refreshed 2026-07 edition now charts K3 — straight onto the Pareto frontier.
#1 on Frontend Code Arena with 1,679 points — ahead of Claude Fable 5 (1,631), GPT-5.6 Sol (1,618), and GLM 5.2 (1,587).
The Kimi track record. Moonshot’s K2.6 still holds the best open SWE-bench Verified score (80.2), and the K2 line has been the default open choice for tool-heavy agent work — see the tier-fair matchups in our Which-LLM guide.

The honest caveats, same as in our living reports: the index is one composite and task-specific rankings differ, and these are launch-week numbers, mostly on Moonshot’s own serving stack. The weights question resolved on schedule: K3 is downloadable from Hugging Face under Moonshot’s own Kimi K3 License (not the K2 line’s Modified MIT — read the text before self-hosting), and K3 now has its row in State of Open Weights.

What it changed

When K3 was announced, every model above GLM 5.2’s intelligence score was closed, and the open-vs-closed gap had sat at 5+ index points all year. With the weights shipped, the open ceiling is 57 — and after Claude Opus 5 (61) and GPT-5.6 Sol (59) landed in the same month, the gap to the very top stands at 4 index points, the narrowest since the GLM-5 release in February (Artificial Analysis). K3’s $3/$15 list price still undercuts every closed model in its class, and it walked straight onto the Pareto frontier.

Kimi K3 API pricing

Moonshot’s list price for the K3 API is $3 input / $15 output per 1M tokens — undercutting every closed model in its class, but 3–4× the K2 line’s price: the first open flagship priced like a closed mid-tier model (Price Tracker). On CheapestInference that call is made: K3 debuted in its own Flagship Pool, flat-rate from $199/mo, unlimited usage during your reserved hours and very limited seats — the pools page is always the live source for lineup, prices and availability.

Unlimited Kimi K3 API access

Live now. The capacity we secured before launch is serving K3 today in the Flagship Pool — we didn’t start the clock the day the weights dropped.
The usual: one OpenAI- and Anthropic-compatible API, flat-rate time-block subscriptions, no token caps during your reserved hours — so it drops into Claude Code, Cline, or any compatible client; it’s in GET /v1/models now.

If you want to be running K3 this week, create an account — seats in the Flagship Pool are very limited, no waitlist.

CheapestInference serves Kimi K3 (Flagship Pool, from $199/mo), GLM 5.3 and MiniMax M3 (Frontier Pool, from $60.35/mo billed annually) and DeepSeek V4.1 Flash and MiMo V2.6 Flash (Core Pool, from $18.70/mo) through one OpenAI- and Anthropic-compatible API on unlimited time-block subscriptions. See the pools or get started.