Model comparison
Kimi K2.7 Code vs Qwen2.5 Coder 32B Instruct
Head-to-head evidence from 4 shared benchmark results across 2 categories. Overall scores shown here use the public BenchAlign v5 ranking lane.
Public leaderboard positions: Kimi K2.7 Code #92 (Estimated); Qwen2.5 Coder 32B Instruct #195 (Supported). Intervals and evidence labels describe ranking uncertainty, not a guarantee for a specific workload.
Evidence parity. Kimi K2.7 Code and Qwen2.5 Coder 32B Instruct share 4 comparable benchmark results. 0 of 8 categories are comparable. 19 results are unique to Kimi K2.7 Code; 0 to Qwen2.5 Coder 32B Instruct.
Updated July 27, 2026- Shared results
- 4
- Kimi K2.7 Code only
- 19
- Qwen2.5 Coder 32B Instruct only
- 0
- Comparable categories
- 0 / 8
Benchmark data for Kimi K2.7 Code and Qwen2.5 Coder 32B Instruct is coming soon on BenchLM.
Confidence note. This is a partial-evidence comparison with 4 shared benchmark results across 2 evidence categories; 0 of 8 categories currently have scoreable aggregates for both models. Treat the verdict as directional until coverage is more balanced.
Why this result
BenchLM has partial data for these models, but not enough overlapping benchmark coverage to produce a fair score-level comparison yet.
Kimi K2.7 Code is priced at $0.95 input / $4.00 output per 1M tokens, versus $0.00 input / $0.00 output per 1M tokens for Qwen2.5 Coder 32B Instruct. Kimi K2.7 Code has the larger context window at 256K, compared with 128K for Qwen2.5 Coder 32B Instruct.
Category breakdown
Exact category averages are shown below. Not measured means BenchLM does not have enough sourced public coverage for that model and category.
Operational comparison
Runtime and commercial metrics are compared only when both models have a complete sourced value.
| Metric | Kimi K2.7 Code | Qwen2.5 Coder 32B Instruct | Comparison |
|---|---|---|---|
| Input / output priceUSD per 1M tokens | Kimi K2.7 Code$0.95 input / $4 output | Qwen2.5 Coder 32B Instruct$0 input / $0 output | Qwen2.5 Coder 32B Instruct has the lower combined listed price. |
| Generation speedtokens per second | Kimi K2.7 CodeNot available | Qwen2.5 Coder 32B InstructNot available | A complete speed comparison is not available. |
| First-answer latencyseconds to first token | Kimi K2.7 CodeNot available | Qwen2.5 Coder 32B InstructNot available | A complete latency comparison is not available. |
| Context windowmaximum listed tokens | Kimi K2.7 Code256K | Qwen2.5 Coder 32B Instruct128K | Kimi K2.7 Code lists the larger context window. |
Benchmark Deep Dive
Agentic7 benchmarks
| Benchmark | Kimi K2.7 Code | Qwen2.5 Coder 32B Instruct | Result |
|---|---|---|---|
| Kimi Claw 24/7Source | 46.9% | — | Not comparable |
| MCP AtlasSource | 76% | — | Not comparable |
| MCP Mark VerifiedSource | 81.1% | — | Not comparable |
| AA Agentic IndexSource | 29.6% | — | Not comparable |
| τ²-bench resultsSource | 90.1% | — | Not comparable |
| GDPval-AASource | 34.3% | — | Not comparable |
| GDPval-AASource | 1186 | — | Not comparable |
Coding6 benchmarks
| Benchmark | Kimi K2.7 Code | Qwen2.5 Coder 32B Instruct | Result |
|---|---|---|---|
| Kimi Code Bench v2Source | 62.0% | — | Not comparable |
| ProgramBenchSource | 53.6% | — | Not comparable |
| MLS-Bench LiteSource | 35.1% | — | Not comparable |
| cursorBench32Source | 49.7% | — | Not comparable |
| AA Coding IndexSource | 60.8% | — | Not comparable |
| AA-SciCodeSource | 47.5% | 27.1% | Kimi K2.7 Code leads |
Reasoning2 benchmarks
Knowledge6 benchmarks
| Benchmark | Kimi K2.7 Code | Qwen2.5 Coder 32B Instruct | Result |
|---|---|---|---|
| Artificial Analysis Intelligence IndexSource | 42.0% | 7.1% | Kimi K2.7 Code leads |
| AA-GPQA DiamondSource | 89.6% | 41.7% | Kimi K2.7 Code leads |
| AA-HLESource | 32.8% | 3.8% | Kimi K2.7 Code leads |
| AA-Omniscience IndexSource | -10.7% | — | Not comparable |
| AA-Omniscience AccuracySource | 38.6% | — | Not comparable |
| AA-Omniscience Hallucination RateSource | 80.3% | — | Not comparable |
Multimodal1 benchmarks
| Benchmark | Kimi K2.7 Code | Qwen2.5 Coder 32B Instruct | Result |
|---|---|---|---|
| Design Arena WebsiteSource | 1300 | — | Not comparable |
Inst. Following1 benchmarks
| Benchmark | Kimi K2.7 Code | Qwen2.5 Coder 32B Instruct | Result |
|---|---|---|---|
| AA-IFBenchSource | 63.1% | — | Not comparable |
Frequently Asked Questions (3)
Can I compare Kimi K2.7 Code and Qwen2.5 Coder 32B Instruct on BenchLM yet?
Not fully yet. BenchLM is tracking both models, but the sourced benchmark breakdown for this comparison is still coming soon.
Why does this comparison show “coming soon”?
BenchLM only shows category winners and benchmark-level calls when we have sourced results that can be compared fairly. For these models, the public benchmark coverage is not complete enough yet.
What data is available for Kimi K2.7 Code and Qwen2.5 Coder 32B Instruct today?
Kimi K2.7 Code: $0.95 input / $4.00 output per 1M tokens Qwen2.5 Coder 32B Instruct: $0.00 input / $0.00 output per 1M tokens Both model pages still include creator, context window, reasoning mode, and other metadata while benchmark coverage fills in.
Self-host vs API cost
Estimates at 50,000 req/day · 1000 tokens/req average.
Related Comparisons
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The model to choose, the cheaper alternative, and the release we would wait on.
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