Kimi K2.7 Code vs gpt-oss-120b
At a Glance
| Compare |  Kimi K2.7 CodeMoonshot AI |  gpt-oss-120bOpenAI |
|---|---|---|
| Intelligence, Cost, and Efficiency | ||
| CostLower is better · Published-token output estimate | #9 of 44$0.043 per LiveBench case | UnrankedNot in the 44-model eligible cohort |
| Pricing and Limits | ||
| Input priceFrom · USD / 1M tokens | $0.68Deepinfra ↗ · Sep 22, 2026 | $0.037Deepinfra ↗ · Sep 23, 2026 |
| Output priceFrom · USD / 1M tokens | $3.30Openrouter ↗ · Sep 23, 2026 | $0.17Deepinfra ↗ · Sep 23, 2026 |
| Context windowMaximum documented tokens | 262K | 131K |
| Model facts checked | Sep 3, 2026View model evidence → | Aug 28, 2026View model evidence → |
Token prices are the lowest available sourced USD rates; input and output may use different providers. Cost ranking estimates output spend on LiveBench, not a full request bill. Ranking methodology →
Available Benchmarks
Side-by-Side Facts
| Field | Kimi K2.7 Code | gpt-oss-120b |
|---|---|---|
| Developer | Moonshot AI | OpenAI |
| Family | Kimi K2 7 | Gpt Oss 120b |
| Model | Kimi K2.7 Code | gpt-oss-120b |
| Version | Kimi K2.7 Code | gpt-oss-120b |
| Lifecycle | active | active |
| Released | 2026-06-11 | Unknown |
| Knowledge cutoff | Unknown | Unknown |
| Input modalities | Text, Image, Video | Text |
| Output modalities | Text | Text |
| Context window | 262K | 131K |
| Total parameters | 1T | 116.8B |
| Active parameters | 32B | 5.1B |
| License | modified-mit | apache-2.0 |
| Open weights | Yes | Yes |
| API available | Yes | Yes |
| Self-hostable | Yes | Yes |
| Provider access | Deepinfra (Standard), Fireworks Ai (Standard), Openrouter (Standard), Together Ai (Standard) | Cerebras (Standard), Deepinfra (Standard), Fireworks Ai (Serverless, Standard), Groq (Standard), Hugging Face (Standard), Openrouter (Standard), Together Ai (Serverless, Standard) |
| Capabilities | agents, chat, coding, reasoning, tools, vision | chat, generation, reasoning, tools |
Kimi K2.7 Code Capabilities
gpt-oss-120b Capabilities
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Primary Evidence
Sources and Freshness
Questions
Kimi K2.7 Code vs gpt-oss-120b FAQs
Is Kimi K2.7 Code or gpt-oss-120b better for coding?+
This comparison does not currently contain a protocol-matched coding benchmark for both Kimi K2.7 Code and gpt-oss-120b, so Model Markets cannot name a coding leader from pricing, context size, or capability labels alone.
Which is cheaper, Kimi K2.7 Code or gpt-oss-120b?+
Kimi K2.7 Code is $0.68 and gpt-oss-120b is $0.037 per million tokens, so gpt-oss-120b is cheaper on this metric. Kimi K2.7 Code is $3.30 and gpt-oss-120b is $0.17 per million tokens, so gpt-oss-120b is cheaper on this metric.
Which has a larger context window, Kimi K2.7 Code or gpt-oss-120b?+
Kimi K2.7 Code has the larger sourced context window. Kimi K2.7 Code supports 262K and gpt-oss-120b supports 131K.
Which performs better in benchmarks, Kimi K2.7 Code or gpt-oss-120b?+
There is no overall benchmark winner: At least two independently verified, protocol-matched benchmarks are required for an overall winner.
Can Kimi K2.7 Code or gpt-oss-120b be self-hosted?+
Both models have the same recorded self-hosting status: supported. Kimi K2.7 Code is open weight; gpt-oss-120b is open weight.
Can Kimi K2.7 Code and gpt-oss-120b understand images?+
Kimi K2.7 Code is documented with image input; gpt-oss-120b is not documented with image input. This reflects supported input modalities, not vision quality.
Which can generate longer answers, Kimi K2.7 Code or gpt-oss-120b?+
Neither has a larger sourced maximum output. Kimi K2.7 Code is — and gpt-oss-120b is —.
Do Kimi K2.7 Code and gpt-oss-120b support reasoning and tool use?+
Kimi K2.7 Code: reasoning, tool calling, and image input. gpt-oss-120b: reasoning and tool calling. Feature support does not establish relative quality.
Which is available from more inference providers, Kimi K2.7 Code or gpt-oss-120b?+
Kimi K2.7 Code has 4 sourced provider routes; gpt-oss-120b has 7, so gpt-oss-120b has broader tracked availability.
Which offers better value, Kimi K2.7 Code or gpt-oss-120b?+
There is no universal value winner. Compare the input and output prices above with the matched benchmark result for your workload: cheaper tokens can be offset by different quality, token usage, latency, or provider availability.