Kimi K2.7 Code vs GPT-4o Mini
At a Glance
| Compare |  Kimi K2.7 CodeMoonshot AI |  GPT-4o MiniOpenAI |
|---|---|---|
| Intelligence, Cost, and Efficiency | ||
| CostLower is better · Published-token output estimate | #9 of 44$0.042 per LiveBench case | UnrankedNot in the 44-model eligible cohort |
| Pricing and Limits | ||
| Input priceFrom · USD / 1M tokens | $0.68Deepinfra ↗ · Sep 22, 2026 | $0.075Openrouter ↗ · Sep 22, 2026 |
| Output priceFrom · USD / 1M tokens | $3.21Openrouter ↗ · Sep 22, 2026 | $0.30Openrouter ↗ · Sep 22, 2026 |
| Context windowMaximum documented tokens | 262K | 128K |
| Model facts checked | Sep 3, 2026View model evidence → | Aug 29, 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-4o Mini |
|---|---|---|
| Developer | Moonshot AI | OpenAI |
| Family | Kimi K2 7 | Gpt 4o |
| Model | Kimi K2.7 Code | GPT-4o Mini |
| Version | Kimi K2.7 Code | GPT-4o Mini |
| Lifecycle | active | active |
| Released | 2026-06-11 | Unknown |
| Knowledge cutoff | Unknown | 2023-10-01 |
| Input modalities | Text, Image, Video | Text, Image |
| Output modalities | Text | Text |
| Context window | 262K | 128K |
| Total parameters | 1T | Unknown |
| Active parameters | 32B | Unknown |
| License | modified-mit | Unknown |
| Open weights | Yes | No |
| API available | Yes | Yes |
| Self-hostable | Yes | No |
| Provider access | Deepinfra (Standard), Fireworks Ai (Standard), Openrouter (Standard), Together Ai (Standard) | Openai (Standard), Openrouter (Standard) |
| Capabilities | agents, chat, coding, reasoning, tools, vision | chat, generation, tools |
Kimi K2.7 Code Capabilities
GPT-4o Mini Capabilities
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Primary Evidence
Sources and Freshness
Questions
Kimi K2.7 Code vs GPT-4o Mini FAQs
Is Kimi K2.7 Code or GPT-4o Mini better for coding?+
This comparison does not currently contain a protocol-matched coding benchmark for both Kimi K2.7 Code and GPT-4o Mini, 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-4o Mini?+
Kimi K2.7 Code is $0.68 and GPT-4o Mini is $0.075 per million tokens, so GPT-4o Mini is cheaper on this metric. Kimi K2.7 Code is $3.21 and GPT-4o Mini is $0.30 per million tokens, so GPT-4o Mini is cheaper on this metric.
Which has a larger context window, Kimi K2.7 Code or GPT-4o Mini?+
Kimi K2.7 Code has the larger sourced context window. Kimi K2.7 Code supports 262K and GPT-4o Mini supports 128K.
Which performs better in benchmarks, Kimi K2.7 Code or GPT-4o Mini?+
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-4o Mini be self-hosted?+
Kimi K2.7 Code is the only model in this pair currently marked as self-hostable. Kimi K2.7 Code is open weight; GPT-4o Mini is not marked open weight.
Can Kimi K2.7 Code and GPT-4o Mini understand images?+
Kimi K2.7 Code is documented with image input; GPT-4o Mini is documented with image input. This reflects supported input modalities, not vision quality.
Which can generate longer answers, Kimi K2.7 Code or GPT-4o Mini?+
Neither has a larger sourced maximum output. Kimi K2.7 Code is — and GPT-4o Mini is 16K.
Do Kimi K2.7 Code and GPT-4o Mini support reasoning and tool use?+
Kimi K2.7 Code: reasoning, tool calling, and image input. GPT-4o Mini: tool calling and image input. Feature support does not establish relative quality.
Which is available from more inference providers, Kimi K2.7 Code or GPT-4o Mini?+
Kimi K2.7 Code has 4 sourced provider routes; GPT-4o Mini has 2, so Kimi K2.7 Code has broader tracked availability.
Which offers better value, Kimi K2.7 Code or GPT-4o Mini?+
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.