Kimi K2 0711

All-versions archive #140Official publisher releaseRestricted license

Moonshot AI·moonshotai/kimi-k2

Kimi K2 0711 is ModelCap archive rank #140 (Modeled · peer benchmarks), from the ModelCap Index as of 22 Sept 2026, 02:49 UTC.

Kimi K2 0711 listed OpenRouter input price is $0.57 per 1M tokens and listed output price is $2.30 per 1M tokens, from OpenRouter listed prices as of 22 Sept 2026, 02:49 UTC.

Kimi K2 0711 has a published context limit of 131K tokens in the ModelCap catalogue as of 22 Sept 2026, 02:49 UTC.

Kimi K2 0711 has 1 published OpenRouter serving endpoint, from OpenRouter as of 22 Sept 2026, 02:49 UTC.

Kimi K2 0711 weight access is restricted license, from ModelCap classification of published repository metadata as of 22 Sept 2026, 02:49 UTC.

Snapshot facts · download the public dataset · methodology

ModelCap Index · All-versions archive

54.7#140

Estimated · 40% support · score interval 42.3–67.1

No admitted evaluation publication timestamp yet

Market Gravity

38.40.1

Secondary market signal

Superseded by Kimi K3. It no longer holds a current-board position; its all-versions archive rank is #140.

Input

$0.57

per 1M tokens

Output

$2.30

per 1M tokens

Context

131K

tokens

Providers

endpoint

Gateway spend

—

latest · Vercel share

Providers

Uptime measured over the last 30 minutes

Provider	Input	Output	Context	Quant	Uptime
Novitafp8	$0.57	$2.30	131K	fp8	100.0%

Hugging Face Inference Providers

Exact repository identity

Provider economics and telemetry published by the Hugging Face Router. These are operational signals, not benchmark results.

Provider	Input	Output	Context	TTFT	Throughput	Features
novita live	$0.57	$2.30	131K	873 ms	54.8 tok/s	tools
featherless-ai live	—	—	—	—	—	—

Reported evaluation leads (6)

Not independent measurement

Structured rows reported in this exact model repository at the pinned revision below. They are the publisher talking about itself: never measured evidence, never a score input on their own. Rows marked as used were laddered against independently measured peers inside the launch-card channel of this model's modeled placement, with the replay-measured optimism haircut applied; the others played no part.

Dataset / task	Metric	Value	Revision	Provenance
harborframework/terminal-bench-2.0 terminalbench_2	—	27.8	Unpinned `not published`	Reported Not used in launch estimate
mercor/apex-agents apex-agents	—	4.1	Unpinned `not published`	Reported Not used in launch estimate
mercor/APEX-SWE apex-swe	—	11.2	Unpinned `not published`	Reported Not used in launch estimate
ScaleAI/SWE-bench_Pro SWE_Bench_Pro	—	27.67	Unpinned `not published`	Reported Not used in launch estimate
SWE-bench/SWE-bench_Multilingual swe_bench_multilingual_%_resolved	—	47.3	Unpinned `not published`	Reported Used in launch estimate
TIGER-Lab/MMLU-Pro mmlu_pro	—	81	Unpinned `not published`	Reported Used in launch estimate

Other versions (4)

ModelCap Index · All-versions archive

Methodology

54.7

Modeled · peer benchmarks · calibrated architecture-and-release-era fallback from static public metadata

All-versions archive

#140

Public rank basis: Modeled · peer benchmarks (architecture)
Index support: 40.0% · limited
Index interval: 42.3–67.1
Rank posterior: Not available
Top 5 / Top 10 probability: Not available / Not available
Posterior as of: Snapshot timestamp unavailable
Evidence as of: No admitted evaluation publication timestamp
Identity binding: Exact catalogue product · moonshotai/moonshotai/kimi-k2/fp8 · aggregates disclosed benchmark configurations · endpoint configuration not claimed · artifact metadata moonshotai/Kimi-K2-Instruct@fd1984e2b7a3350dbf7305fe73a4ede25c14de50 (not the evaluation revision)
Index observations used: 0
Qualified Index evidence point: 54.7

How this position was produced · fused launch placement

No general-family board lists this model yet. Its point estimate is the precision-weighted mean of every channel that spoke for it; each channel’s share is the weight it carried. Independent measurements replace this placement as boards list the model.

Fused estimate: 54.7 ± 9.7
Independent share: 0%
Shrink toward corpus: +2.8
Peer ceiling share: 79%
Optimism haircut: −4.6 points · cross lab probe, 2 probes
Method: precision-weighted-v1

Channels fused into this model’s launch placement
Channel	Estimate	Share
Publisher corpus prior · not independent publisher-corpus-prior over 203 held-out anchors	67.3 ± 22.7	18%
Launch card against resolved peers · not independent launch card against 3 resolved peers on 14 rows	51.8 ± 10.7	82%

· Launch card row aime-2024: claims above every resolved peer
· Launch card row aime-2025: claims above every resolved peer
· Launch card row gpqa-diamond: claims above every resolved peer
· Launch card row hle: inside the resolved peer range
· Launch card row hmmt-2025: claims above every resolved peer
· Launch card row ifeval: claims above every resolved peer
· Launch card row livecodebench-v-6: claims above every resolved peer
· Launch card row math-500: claims above every resolved peer
· Launch card row mmlu-pro: inside the resolved peer range
· Launch card row mmlu-redux: claims above every resolved peer
· Launch card row mmlu: inside the resolved peer range
· Launch card row swe-bench-multilingual: claims above every resolved peer
· Launch card row swe-bench-verified: claims above every resolved peer
· Launch card row terminalbench: claims above every resolved peer

Bayesian Evidence Score · measured evidence input only

BES normalizes admitted public benchmark evidence for measured Index rows. Its legacy score and observables prior do not define the public language rank.

Observed capability: —
Evidence status: No admitted measured evidence
General preference: —
Coding: —
Agents & tools: —
Reasoning: —
Evidence breadth: 0%
Evidence coverage: 0%

Market and catalogue signals · secondary, never the language Index rank

Usage (OpenRouter popularity): 10.2
Liquidity (providers × uptime): 97.4
Open reach (HF downloads): —
Surface (context / tools / modalities): 45.2
Freshness: 18.9

· OpenRouter popularity #179
· 1 live provider
· Catalogue: context 131072, tools
· First seen 2025-07-11

These adoption and deployment observations remain context only. They do not change this model's ModelCap Index score or rank.

The bounded 0–100 capability score blends each source’s competitive placement with its published achievement, then combines capability families. Evidence breadth adds a modest uncertainty adjustment; price and popularity are not part of benchmark-led rank.

Experimental capability estimate

Estimate policy

44.2

28.0–60.3 uncertainty interval

Excluded from rank

Confidence: 74%
Training support: 205 models · 119 lineages
Out-of-domain check: in domain
Feature coverage: 100%
Lineage-held-out error: 10.0 MAE

Experimental estimate. This layer predicts from admitted evidence and safe metadata only. It never enters ModelCap Score or rank.

Deployment readiness

Metadata index

62.0

partial · 71% metadata coverage

Not capability

Artifact reproducibility: 85.0
Access & legal clarity: 67.5
Deployability: 76.5
Evaluation provenance: 0.0

Missing: artifactReproducibility.base-lineage, accessLegalClarity.resolvable-license-terms, evaluationProvenance.admitted-evaluation-observations, evaluationProvenance.capability-family-breadth, evaluationProvenance.evaluation-source-diversity, evaluationProvenance.identity-and-source-confidence, evaluationProvenance.evaluation-dataset-revisions.

A metadata completeness and deployability index—not a safety certification, quality grade, or production-readiness claim.

Market activity

Venue coverage

Market Gravity: 38.4
OpenRouter weekly popularity: #179 of 322

Market Gravity uses OpenRouter’s full-catalogue popularity order. Sparse Vercel top-ten observations appear only when published and do not affect the score.

Arena preference

No matched Arena rating

This model is visible because it is available in the live catalogue, but it has no trustworthy community result yet. ModelCap does not infer a quality score from price, features, or market activity.

Market Gravity breakdown

Usage55%: 10.2
Liquidity25%: 97.4
Open reach15%: 50.0
Freshness5%: 18.9

Specification

Context window: 131K tokens
Max output: 98K tokens
Inputs: Text
Outputs: Text
Tokenizer: Other
Cached input tokens: Not offered
First seen on OpenRouter: 11 Jul 2025
Knowledge cutoff: 2024-12-31

Capabilities

Not supported: Reasoning
Supported: Tool use
Not supported: Structured output
Not supported: Response format
Not supported: Moderated

Weights & access

Restricted license. Downloadable weights use a model-specific or use-restricted license. Review the linked license before use, redistribution, or commercial deployment.

Access: Restricted license
Licence: modified-mit
Pinned revision: fd1984e2b7a3
Parameters: 1026.4B
Architecture: DeepseekV3ForCausalLM
Model type: kimi_k2
Hugging Face downloads (30d): 222K
Downloads (all time): 4.2M
Likes: 3K
Repository: moonshotai/Kimi-K2-Instruct
Repository updated: 23 Apr 2026, 02:08 UTC

Benchmark leaderboards featuring Kimi K2 0711

All leaderboards

Public benchmark boards on which Kimi K2 0711 has a published result
Leaderboard	Score	Source rank	Configuration	Published
τ²-Bench Telecom (Artificial Analysis run)Artificial Analysis	61.1%	#187 of 436	tau2:telecom:dual-control:pass-at-1:3-repeats:source-model="Kimi K2":reasoning=false	5 Sept 2026
Humanity's Last Exam (Artificial Analysis run)Artificial Analysis	7.4%	#334 of 615	hle:may-2025:text-only-2158:no-tools:pass-at-1:source-model="Kimi K2":reasoning=false	5 Sept 2026

Kimi K2 0711: common questions

How does Kimi K2 0711 rank among AI models?

Kimi K2 0711 is a superseded version and is not on the current board as of 22 September 2026. Its archive position, where one exists, is shown on the page; the newer version carries the current rank.

How much does Kimi K2 0711 cost per 1M tokens?

Kimi K2 0711 is listed at $0.57 per 1M input tokens and $2.30 per 1M output tokens as of 22 September 2026. Prices come from the OpenRouter catalogue and re-observe on every refresh. For 1,000 requests using 2,000 input and 500 output tokens each, the listed rates imply $2.29 total (2M input + 0.5M output). This excludes caching, batch discounts, prompt-length tiers, tool charges and retries; verify the chosen endpoint's rate and limits before budgeting.

What is the context window of Kimi K2 0711?

Kimi K2 0711 has a published context window of 131K tokens (131,072) and a maximum output of 98K tokens. Individual providers can serve less than the published maximum; the providers table lists each endpoint's own limit.

Is Kimi K2 0711 open-weight?

Restricted license (modified-mit). Downloadable weights use a model-specific or use-restricted license. Review the linked license before use, redistribution, or commercial deployment. The repository is moonshotai/Kimi-K2-Instruct on Hugging Face.

Which API providers serve Kimi K2 0711?

One provider serves Kimi K2 0711 through OpenRouter as of 22 September 2026: Novita. Each provider's price, context limit, quantization and measured uptime are in the providers table above.

Which benchmarks has Kimi K2 0711 been evaluated on?

Kimi K2 0711 has published results on 2 public boards tracked by ModelCap as of 22 September 2026: AA τ²-Bench Telecom 61.1% (#187 of 436) and AA Humanity's Last Exam 7.4% (#334 of 615). Each board page ranks every tracked model on that benchmark; the ModelCap Index combines them with published uncertainty.

When was Kimi K2 0711 released?

Kimi K2 0711 first appeared in the catalogue on 11 Jul 2025, with a published knowledge cutoff of 2024-12-31. It has since been superseded by Kimi K3. Rank and price movements since then are recorded on the site's changes feed.