Model comparison
Kimi K2.6 vs Qwen2.5 Coder 32B Instruct
Head-to-head evidence from 4 shared benchmark results across 2 categories. Overall scores shown here use BenchLM's provisional ranking lane.
Verified leaderboard positions: Kimi K2.6 #13; Qwen2.5 Coder 32B Instruct unranked
Evidence parity. Kimi K2.6 and Qwen2.5 Coder 32B Instruct share 4 comparable benchmark results. 0 of 8 categories are comparable. 56 results are unique to Kimi K2.6; 0 to Qwen2.5 Coder 32B Instruct.
Updated July 13, 2026- Shared results
- 4
- Kimi K2.6 only
- 56
- Qwen2.5 Coder 32B Instruct only
- 0
- Comparable categories
- 0 / 8
Benchmark data for Kimi K2.6 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 K2.6 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 K2.6 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.
| Category | Kimi K2.6 | Δ | Qwen2.5 Coder 32B Instruct |
|---|---|---|---|
| Agentic | Kimi K2.673.5 | MarginNo overlap | Qwen2.5 Coder 32B InstructNot measured |
| Coding | Kimi K2.672.6 | MarginNo overlap | Qwen2.5 Coder 32B InstructNot measured |
| Knowledge | Kimi K2.642.2 | MarginNo overlap | Qwen2.5 Coder 32B InstructNot measured |
| Math | Kimi K2.667.1 | MarginNo overlap | Qwen2.5 Coder 32B InstructNot measured |
| Multimodal | Kimi K2.679.8 | MarginNo overlap | Qwen2.5 Coder 32B InstructNot measured |
Operational comparison
Runtime and commercial metrics are compared only when both models have a complete sourced value.
| Metric | Kimi K2.6 | Qwen2.5 Coder 32B Instruct | Comparison |
|---|---|---|---|
| Input / output priceUSD per 1M tokens | Kimi K2.6$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 K2.6Not available | Qwen2.5 Coder 32B InstructNot available | A complete speed comparison is not available. |
| First-answer latencyseconds to first token | Kimi K2.6Not available | Qwen2.5 Coder 32B InstructNot available | A complete latency comparison is not available. |
| Context windowmaximum listed tokens | Kimi K2.6256K | Qwen2.5 Coder 32B Instruct128K | Kimi K2.6 lists the larger context window. |
Benchmark Deep Dive
Agentic22 benchmarks
| Benchmark | Kimi K2.6 | Qwen2.5 Coder 32B Instruct | Result |
|---|---|---|---|
| Terminal-Bench 2.0Source | 66.7% | — | Not comparable |
| BrowseCompSource | 83.2% | — | Not comparable |
| OSWorld-VerifiedSource | 73.1% | — | Not comparable |
| ToolathlonSource | 50% | — | Not comparable |
| MCP AtlasSource | 55.9% | — | Not comparable |
| Claw-EvalSource | 62.3% | — | Not comparable |
| DeepSearchQASource | 92.5% | — | Not comparable |
| WideResearchSource | 80.8% | — | Not comparable |
| AA Agentic IndexSource | 30.3% | — | Not comparable |
| Tau2-TelecomSource | 95.9% | — | Not comparable |
| GDPval-AASource | 34.5% | — | Not comparable |
| GDPval-AASource | 1190 | — | Not comparable |
| APEX-Agents-AASource | 28.5% | — | Not comparable |
| Gert LabsSource | 56.82% | — | Not comparable |
| ResearchClawBenchSource | 18.0% | — | Not comparable |
| OSWorld 2.0Source | 4.6% | — | Not comparable |
| AA BriefcaseSource | 809 | — | Not comparable |
| AA AutomationBenchSource | 19.6% | — | Not comparable |
| AA EnterpriseOps-GymSource | 38.5% | — | Not comparable |
| AA Harvey LABSource | 0.0% | — | Not comparable |
| AA ITBenchSource | 31.2% | — | Not comparable |
| AA Tau3 BankingSource | 20.6% | — | Not comparable |
Coding13 benchmarks
| Benchmark | Kimi K2.6 | Qwen2.5 Coder 32B Instruct | Result |
|---|---|---|---|
| SWE-bench VerifiedSource | 80.2% | — | Not comparable |
| LiveCodeBenchSource | 89.6% | — | Not comparable |
| LiveCodeBench v6Source | 89.6% | — | Not comparable |
| SWE-bench ProSource | 58.6% | — | Not comparable |
| SWE MultilingualSource | 76.7% | — | Not comparable |
| SciCodeSource | 52.2% | — | Not comparable |
| Terminal-Bench 2.0Source | 66.7% | — | Not comparable |
| Vibe Code BenchSource | 37.89% | — | Not comparable |
| cursorBench31Source | 47.6% | — | Not comparable |
| AA Coding IndexSource | 61.8% | — | Not comparable |
| Terminal-Bench HardSource | 43.9% | — | Not comparable |
| AA-SciCodeSource | 53.5% | 27.1% | Kimi K2.6 leads |
| AA Terminal-Bench 2.1Source | 65.9% | — | Not comparable |
Reasoning2 benchmarks
Knowledge10 benchmarks
| Benchmark | Kimi K2.6 | Qwen2.5 Coder 32B Instruct | Result |
|---|---|---|---|
| GPQASource | 90.5% | — | Not comparable |
| GPQA-DSource | 90.5% | — | Not comparable |
| HLESource | 34.7% | — | Not comparable |
| Artificial Analysis Intelligence IndexSource | 44.2% | 7.1% | Kimi K2.6 leads |
| AA-GPQA DiamondSource | 91.1% | 41.7% | Kimi K2.6 leads |
| AA-HLESource | 35.9% | 3.8% | Kimi K2.6 leads |
| AA-Omniscience IndexSource | 6.4% | — | Not comparable |
| AA-Omniscience AccuracySource | 32.8% | — | Not comparable |
| AA-Omniscience Hallucination RateSource | 39.3% | — | Not comparable |
| AA Openness IndexSource | 33.3% | — | Not comparable |
Math5 benchmarks
Multimodal7 benchmarks
| Benchmark | Kimi K2.6 | Qwen2.5 Coder 32B Instruct | Result |
|---|---|---|---|
| MMMU-ProSource | 79.4% | — | Not comparable |
| MMMU-Pro w/ PythonSource | 80.1% | — | Not comparable |
| CharXivSource | 80.4% | — | Not comparable |
| MathVisionSource | 87.4% | — | Not comparable |
| V*Source | 96.9% | — | Not comparable |
| AA-MMMU-ProSource | 79.4% | — | Not comparable |
| Design Arena WebsiteSource | 1318 | — | Not comparable |
Inst. Following1 benchmarks
| Benchmark | Kimi K2.6 | Qwen2.5 Coder 32B Instruct | Result |
|---|---|---|---|
| AA-IFBenchSource | 76.0% | — | Not comparable |
Frequently Asked Questions (3)
Can I compare Kimi K2.6 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 K2.6 and Qwen2.5 Coder 32B Instruct today?
Kimi K2.6: $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
The AI models change fast. We track them for you.
A weekly brief for engineers and researchers covering new models, ranking shifts, and pricing changes.
Free. No spam. Unsubscribe anytime.