Inference availability
Providers Provider-specific identifiers, limits, and listed prices per million tokens. Every row links back to the provider's own documentation.
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Specification
Capabilities Recorded from the source catalog and provider listings.
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Reasoning
Unknown
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Tool calling
Unknown
?
Structured output
Unknown
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Attachments
Unknown
✓
Vision input
Yes
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Open weights
Unknown
Model family Not documented
Knowledge cutoff Not documented
License Not documented
Release date Not documented
Model ID moonshotai/kimi-k2.7-code:batch
Published evaluations
Benchmarks Every result stays attached to its source, version, metric and harness. Scores from different versions are never merged, and the profile below plots each benchmark against its own population rather than on a shared scale.
Benchmark registry
Benchmark profile
Coding IndexCoding Index
Agentic IndexAgentic Index
Design Arena: w…Design Arena: website
IntelligenceIntelligence Index
Design Arena: c…Design Arena: codecategories
Design Arena: d…Design Arena: dataviz
Design Arena: g…Design Arena: gamedev
Design Arena: u…Design Arena: uicomponent
This model — Coding Index: 60.8 index (76.1th percentile)
This model — Agentic Index: 29.6 index (66.7th percentile)
This model — Design Arena: website: 1298 elo (87.4th percentile)
This model — Intelligence Index: 41.9 index (71.0th percentile)
This model — Design Arena: codecategories: 1289 elo (79.3th percentile)
This model — Design Arena: dataviz: 1253 elo (65.2th percentile)
This model — Design Arena: gamedev: 1254 elo (69.9th percentile)
This model — Design Arena: uicomponent: 1294 elo (78.4th percentile)
Each axis is this model's percentile among the 8 benchmarks it has published results for, measured against every other model with a score on that same benchmark. Percentiles are used because benchmarks do not share a scale — a 60 on one is not a 60 on another. Hover any point for the raw score.
Coding Index
#38 of 159
2.7
median 43.9
78.0
Agentic Index
#48 of 147
0.3
median 21.4
55.4
Design Arena: website
#18 of 147
773.0
median 1208.0
1379.0
Intelligence Index
#42 of 145
5.5
median 35.4
60.7
Design Arena: codecategories
#29 of 140
819.0
median 1200.0
1416.0
Design Arena: dataviz
#47 of 138
884.0
median 1199.0
1391.0
Design Arena: gamedev
#41 of 136
812.0
median 1195.5
1394.0
Design Arena: uicomponent
#28 of 132
803.0
median 1203.0
1397.0
Design Arena: 3d
#25 of 130
891.0
median 1194.5
1454.0
Design Arena: svg
#35 of 92
1014.0
median 1198.0
1353.0
Design Arena: asciiart
#19 of 76
1035.0
median 1192.0
1359.0
Design Arena: webapps
#26 of 53
1000.0
median 1227.0
1330.0
Design Arena: fullstack
#21 of 49
948.0
median 1199.0
1503.0
Design Arena: mobileapps
#26 of 49
1114.0
median 1206.0
1280.0
Design Arena: androidnative
#32 of 47
987.0
median 1204.0
1318.0
Design Arena: godotgamedev
#19 of 42
995.0
median 1170.5
1344.0
Design Arena: agenticgamedev
#26 of 31
1012.0
median 1190.0
1302.0
Design Arena: htmlslides
#10 of 31
1046.0
median 1206.0
1260.0
Design Arena: python-pptxslides
#22 of 30
1019.0
median 1191.0
1375.0
Each strip shows every published score for that benchmark, with this model marked. The lighter shape behind the ticks is the density of results, and the dashed line is the median.
Cost against capability
Price and performance Listed input price plotted against Coding Index, the benchmark with the widest published coverage that this model appears in.
0
41
83
$0.01
$0.1
$1
$10
Amazon: Nova 2 Lite — $0.3/M, score 23.0
Claude Fable 5 — $10/M, score 76.5
Anthropic: Claude Fable 5 (batch) — $5/M, score 76.5
Anthropic: Claude Haiku 4.5 — $1/M, score 43.9
Anthropic: Claude Haiku 4.5 (batch) — $0.5/M, score 43.9
Anthropic: Claude Opus 4.7 — $5/M, score 73.6
Anthropic: Claude Opus 4.7 (batch) — $2.5/M, score 73.6
Anthropic: Claude Opus 4.8 — $5/M, score 74.3
Anthropic: Claude Opus 4.8 (batch) — $2.5/M, score 74.3
Claude Opus 5 — $5/M, score 78.0
Claude Opus 5 (batch) — $2.5/M, score 78.0
Anthropic: Claude Sonnet 4 — $3/M, score 37.6
Anthropic: Claude Sonnet 4.5 — $3/M, score 52.1
Anthropic: Claude Sonnet 4.5 (batch) — $1.5/M, score 52.1
Anthropic: Claude Sonnet 4.6 — $3/M, score 63.0
Anthropic: Claude Sonnet 4.6 (batch) — $1.5/M, score 63.0
Claude Sonnet 5 — $2/M, score 71.5
Anthropic: Claude Sonnet 5 (batch) — $1/M, score 71.5
Arcee AI: Trinity Large Thinking — $0.22/M, score 25.8
Cohere: Command A — $2.5/M, score 27.8
DeepSeek: DeepSeek V3 0324 — $0.27/M, score 21.2
DeepSeek-R1 — $0.7/M, score 24.6
DeepSeek: DeepSeek V3.1 Terminus — $0.27/M, score 43.5
DeepSeek: DeepSeek V3.2 — $0.269/M, score 44.2
DeepSeek V4 Flash — $0.14/M, score 56.2
DeepSeek V4 Flash 0731 — $0.09/M, score 69.1
DeepSeek V4 Pro — $0.435/M, score 59.4
Gemini 2.5 Pro — $1.25/M, score 33.3
Google: Gemini 2.5 Pro (batch) — $0.625/M, score 33.3
Gemini 3.1 Flash Lite Preview — $0.25/M, score 34.7
Gemini 3.1 Pro Preview — $2/M, score 68.8
Google: Gemini 3.1 Pro Preview (batch) — $1/M, score 68.8
Gemini 3.5 Flash — $1.5/M, score 70.1
Gemini 3.5 Flash Lite — $0.3/M, score 49.3
Google: Gemini 3.5 Flash Lite (batch) — $0.15/M, score 49.3
Google: Gemini 3.5 Flash (batch) — $0.75/M, score 70.1
Gemini 3.6 Flash — $1.5/M, score 69.2
Google: Gemini 3.6 Flash (batch) — $0.75/M, score 69.2
Google: Gemma 3 12B — $0.05/M, score 5.8
Google: Gemma 3 27B — $0.08/M, score 10.1
Google: Gemma 3 4B — $0.05/M, score 2.7
Google: Gemma 3n 4B — $0.06/M, score 3.2
Gemma 4 26B A4B IT — $0.06/M, score 39.3
Gemma 4 31B IT — $0.09/M, score 43.4
IBM: Granite 4.1 8B — $0.05/M, score 9.5
Inception: Mercury 2 — $0.25/M, score 31.1
inclusionAI: Ling-2.6-flash — $0.01/M, score 25.3
Ling-3.0-flash — $0.075/M, score 50.6
inclusionAI: Ring-2.6-1T — $0.075/M, score 42.8
Kwaipilot: KAT-Coder-Pro V2 — $0.3/M, score 59.5
Meta: Llama 3.1 8B Instruct — $0.05/M, score 5.4
Meta: Llama 3.3 70B Instruct — $0.1/M, score 11.9
Meta: Llama 4 Maverick — $0.2/M, score 16.3
Meta: Llama 4 Scout — $0.1/M, score 8.2
Muse Spark 1.1 — $1.25/M, score 71.3
MiniMax: MiniMax M2.7 — $0.27/M, score 52.6
MiniMax: MiniMax M3 — $0.3/M, score 58.6
MiniMax: MiniMax M3 (batch) — $0.15/M, score 58.6
Mistral: Devstral 2 2512 — $0.4/M, score 31.3
Mistral: Ministral 3 14B 2512 — $0.2/M, score 14.4
Mistral: Ministral 3 3B 2512 — $0.1/M, score 4.8
Mistral: Ministral 3 8B 2512 — $0.15/M, score 9.7
Mistral: Mistral Large 3 2512 — $0.5/M, score 20.1
Mistral: Mistral Medium 3.5 — $1.5/M, score 46.9
Mistral: Mistral Medium 3.1 — $0.4/M, score 20.5
Mistral: Mistral Small 4 — $0.15/M, score 26.6
Kimi K2 Thinking — $0.6/M, score 21.0
Kimi K2.5 — $0.6/M, score 46.8
Kimi K2.6 — $0.95/M, score 61.8
Kimi K2.7 Code — $0.95/M, score 60.8
MoonshotAI: Kimi K2.7 Code (batch) — $0.475/M, score 60.8
Kimi K3 — $3/M, score 76.2
Nex AGI: Nex-N2-Pro — $0.25/M, score 59.1
Nemotron 3 Nano 30B A3B — $0.05/M, score 14.4
Nemotron 3 Super 120B A12B — $0.2/M, score 37.7
Nemotron 3 Ultra 550B A55B — $0.5/M, score 49.3
NVIDIA: Nemotron 3 Ultra (batch) — $0.3/M, score 49.3
GPT-3.5-turbo — $0.5/M, score 10.7
OpenAI: GPT-3.5 Turbo (batch) — $0.25/M, score 10.7
GPT-4 — $30/M, score 13.1
GPT-4 Turbo — $10/M, score 21.5
OpenAI: GPT-4 Turbo (batch) — $5/M, score 21.5
GPT-4.1 mini — $0.4/M, score 20.2
OpenAI: GPT-4.1 Mini (batch) — $0.2/M, score 20.2
GPT-4.1 nano — $0.1/M, score 11.1
OpenAI: GPT-4.1 Nano (batch) — $0.05/M, score 11.1
GPT-4o (2024-05-13) — $5/M, score 24.2
GPT-4o mini — $0.15/M, score 11.4
OpenAI: GPT-4o-mini (batch) — $0.075/M, score 11.4
GPT-5 — $1.25/M, score 37.8
GPT-5 Mini — $0.25/M, score 15.6
OpenAI: GPT-5 Mini (batch) — $0.125/M, score 15.6
GPT-5.1 — $1.25/M, score 49.4
OpenAI: GPT-5.1 (batch) — $0.625/M, score 49.4
GPT-5.4 — $2.5/M, score 71.1
GPT-5.4 mini — $0.75/M, score 56.1
OpenAI: GPT-5.4 Mini (batch) — $0.375/M, score 56.1
GPT-5.4 nano — $0.2/M, score 56.1
OpenAI: GPT-5.4 Nano (batch) — $0.1/M, score 56.1
OpenAI: GPT-5.4 (batch) — $1.25/M, score 71.1
GPT-5.5 — $5/M, score 74.9
OpenAI: GPT-5.5 (batch) — $2.5/M, score 74.9
GPT-5.6 Luna — $0.2/M, score 71.4
OpenAI: GPT-5.6 Luna (batch) — $0.1/M, score 71.4
GPT-5.6 Sol — $5/M, score 77.4
OpenAI: GPT-5.6 Sol (batch) — $2.5/M, score 77.4
GPT-5.6 Terra — $2/M, score 76.7
OpenAI: GPT-5.6 Terra (batch) — $1/M, score 76.7
OpenAI: GPT-5 (batch) — $0.625/M, score 37.8
GPT OSS 120B — $0.03/M, score 30.4
GPT OSS 20B — $0.03/M, score 20.7
o1 — $15/M, score 39.7
OpenAI: o1 (batch) — $7.5/M, score 39.7
OpenAI: o3 Mini High — $1.1/M, score 16.3
OpenAI: o3 Mini High (batch) — $0.55/M, score 16.3
Qwen: Qwen3 14B — $0.227/M, score 13.8
Qwen: Qwen3 235B A22B Thinking 2507 — $0.23/M, score 22.1
Qwen: Qwen3 30B A3B Thinking 2507 — $0.2/M, score 12.1
Qwen: Qwen3 32B — $0.08/M, score 15.3
Qwen: Qwen3 8B — $0.117/M, score 9.0
Qwen: Qwen3 Coder Next — $0.12/M, score 36.2
Qwen: Qwen3 Next 80B A3B Thinking — $0.15/M, score 17.4
Qwen: Qwen3.5-122B-A10B — $0.26/M, score 45.7
Qwen: Qwen3.5-35B-A3B — $0.14/M, score 37.0
Qwen: Qwen3.5 397B A17B — $0.39/M, score 48.2
Qwen: Qwen3.5-9B — $0.1/M, score 28.7
Qwen: Qwen3.6 27B — $0.6/M, score 53.7
Qwen: Qwen3.6 35B A3B — $0.14/M, score 41.9
Qwen: Qwen3.6 Plus — $0.325/M, score 54.5
Qwen: Qwen3.7 Max — $1.475/M, score 66.0
Qwen: Qwen3.7 Plus — $0.32/M, score 55.9
Qwen: Qwen3.8 Max — $2/M, score 71.8
Step 3.7 Flash — $0.185/M, score 39.6
Hy3 preview — $0.063/M, score 58.8
Inkling — $1.87/M, score 52.1
Inkling Small — $0.45/M, score 52.9
Thinking Machines: Inkling (batch) — $0.5/M, score 52.1
Upstage: Solar Pro 3 — $0.15/M, score 16.2
SpaceXAI: Grok 4.3 — $1.25/M, score 42.2
SpaceXAI: Grok 4.5 — $2/M, score 72.4
SpaceXAI: Grok Build 0.1 — $1/M, score 51.5
MiMo-V2.5 — $0.14/M, score 56.8
MiMo-V2.5-Pro — $0.435/M, score 60.2
Z.ai: GLM 4.6 — $0.5/M, score 45.8
Z.ai: GLM 4.7 — $0.4/M, score 45.3
Z.ai: GLM 5.1 — $0.952/M, score 55.8
Z.ai: GLM 5.2 — $0.56/M, score 68.8
Z.ai: GLM 5.2 (batch) — $0.7/M, score 68.8
MoonshotAI: Kimi K2.7 Code (batch)
Input price per million tokens (log scale)
Index
The stepped line is the efficient frontier: at each price, the best score available for that money or less. A model sitting on it is not being beaten by anything cheaper. Price is log-scaled because listed rates span four orders of magnitude. Only models with both a listed price and a score on this benchmark can appear.
Catalog activity
Change log Field-level changes detected between successful source imports.
Full change log No changes recorded This record has not changed within the retained import history.
Provenance
Sources & verification Every figure on this page traces back to one of these records.
Methodology
Last verified Aug 06, 2026
Status Source-linked
Confidence Medium
Catalog source OpenRouter
Public API
Use this record Fetch the complete source-linked model record. No key, no account, no rate-limited tier.
API documentation
Endpoint Copy
GET https://www.modelbench.lol/api/v1/models/moonshotai/kimi-k2.7-code:batchcurl Copy
curl "https://www.modelbench.lol/api/v1/models/moonshotai/kimi-k2.7-code:batch"
Common questions
Frequently asked questions Answered directly from the stored record — nothing here is generated beyond the catalog's own fields.
What is MoonshotAI: Kimi K2.7 Code (batch)?
MoonshotAI: Kimi K2.7 Code is a coding-focused model in Moonshot AI's Kimi K2 family, built to complete end-to-end programming tasks reliably over long contexts. It uses a native multimodal mixture-of-experts. It is published by Moonshot AI and catalogued here from OpenRouter.
What is the context length of MoonshotAI: Kimi K2.7 Code (batch)?
MoonshotAI: Kimi K2.7 Code (batch) accepts up to 262.144K tokens of context.
Does MoonshotAI: Kimi K2.7 Code (batch) support tool calling and structured output?
Provider catalogs list support for image input.
