Model comparison
Kimi K2.5 vs Mistral Large 2
Head-to-head evidence from 11 shared benchmark results across 5 categories. Overall scores shown here use the public BenchAlign v5 ranking lane.
Public leaderboard positions: Kimi K2.5 #54 (Supported); Mistral Large 2 #163 (Estimated). Intervals and evidence labels describe ranking uncertainty, not a guarantee for a specific workload.
Evidence parity. Kimi K2.5 and Mistral Large 2 share 11 comparable benchmark results. 0 of 8 categories are comparable. 52 results are unique to Kimi K2.5; 0 to Mistral Large 2.
Updated July 20, 2026- Shared results
- 11
- Kimi K2.5 only
- 52
- Mistral Large 2 only
- 0
- Comparable categories
- 0 / 8
Benchmark data for Kimi K2.5 and Mistral Large 2 is coming soon on BenchLM.
Confidence note. This is a partial-evidence comparison with 11 shared benchmark results across 5 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 K2.5 has the larger context window at 256K, compared with 128K for Mistral Large 2.
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 K2.5 | Δ | Mistral Large 2 |
|---|---|---|---|
| Agentic | Kimi K2.555.0 | MarginNo overlap | Mistral Large 2Not measured |
| Coding | Kimi K2.559.4 | MarginNo overlap | Mistral Large 2Not measured |
| Reasoning | Kimi K2.561.0 | MarginNo overlap | Mistral Large 2Not measured |
| Knowledge | Kimi K2.556.9 | MarginNo overlap | Mistral Large 2Not measured |
| Math | Kimi K2.560.6 | MarginNo overlap | Mistral Large 2Not measured |
| Multilingual | Kimi K2.582.3 | MarginNo overlap | Mistral Large 2Not measured |
| Multimodal | Kimi K2.578.5 | MarginNo overlap | Mistral Large 2Not measured |
| Inst. Following | Kimi K2.593.9 | MarginNo overlap | Mistral Large 2Not measured |
Operational comparison
Runtime and commercial metrics are compared only when both models have a complete sourced value.
| Metric | Kimi K2.5 | Mistral Large 2 | Comparison |
|---|---|---|---|
| Input / output priceUSD per 1M tokens | Kimi K2.5$0.6 input / $3 output | Mistral Large 2Not available | A complete price comparison is not available. |
| Generation speedtokens per second | Kimi K2.545 tok/s | Mistral Large 238 tok/s | Kimi K2.5 has the higher measured throughput. |
| First-answer latencyseconds to first token | Kimi K2.52.38 s | Mistral Large 21.45 s | Mistral Large 2 reaches the first token sooner. |
| Context windowmaximum listed tokens | Kimi K2.5256K | Mistral Large 2128K | Kimi K2.5 lists the larger context window. |
Benchmark Deep Dive
Agentic19 benchmarks
| Benchmark | Kimi K2.5 | Mistral Large 2 | Result |
|---|---|---|---|
| Terminal-Bench 2.0Source | 50.8% | — | Not comparable |
| BrowseCompSource | 60.6% | — | Not comparable |
| Claw-EvalSource | 52.3% | — | Not comparable |
| QwenClawBenchSource | 54.3% | — | Not comparable |
| τ³-bench resultsSource | 65.7% | — | Not comparable |
| DeepSearchQASource | 77.1% | — | Not comparable |
| DeepPlanningSource | 14.4% | — | Not comparable |
| ToolathlonSource | 27.8% | — | Not comparable |
| MCP AtlasSource | 29.5% | — | Not comparable |
| MCP-TasksSource | 59.1% | — | Not comparable |
| WideResearchSource | 72.7% | — | Not comparable |
| τ²-bench resultsSource | 95.9% | 30.7% | Kimi K2.5 leads |
| APEX-Agents-AASource | 11.5% | — | Not comparable |
| Gert LabsSource | 45.88% | — | Not comparable |
| ResearchClawBenchSource | 14.0% | — | Not comparable |
| JobBenchSource | 8.7% | — | Not comparable |
| AA Agentic IndexSource | 21.7% | — | Not comparable |
| GDPval-AASource | 25.4% | — | Not comparable |
| GDPval-AASource | 1009 | — | Not comparable |
Coding10 benchmarks
| Benchmark | Kimi K2.5 | Mistral Large 2 | Result |
|---|---|---|---|
| SWE-bench VerifiedSource | 76.8% | — | Not comparable |
| SWE-bench Verified*Source | 70.8% | — | Not comparable |
| LiveCodeBench v6Source | 85.0% | — | Not comparable |
| SWE-bench ProSource | 50.7% | — | Not comparable |
| SWE MultilingualSource | 73% | — | Not comparable |
| SWE-RebenchSource | 58.5% | — | Not comparable |
| React Native EvalsSource | 77.2% | — | Not comparable |
| SciCodeSource | 48.7% | — | Not comparable |
| AA-SciCodeSource | 49.0% | 29.2% | Kimi K2.5 leads |
| AA Coding IndexSource | 46.8% | — | Not comparable |
Reasoning3 benchmarks
Knowledge12 benchmarks
| Benchmark | Kimi K2.5 | Mistral Large 2 | Result |
|---|---|---|---|
| GPQASource | 87.6% | — | Not comparable |
| GPQA-DSource | 87.6% | — | Not comparable |
| SuperGPQASource | 69.2% | — | Not comparable |
| MMLU-ProSource | 87.1% | — | Not comparable |
| MMLU-Pro (Arcee)Source | 87.1% | — | Not comparable |
| HLESource | 30.1% | — | Not comparable |
| Artificial Analysis Intelligence IndexSource | 35.4% | 9.2% | Kimi K2.5 leads |
| AA-GPQA DiamondSource | 87.9% | 48.6% | Kimi K2.5 leads |
| AA-HLESource | 29.4% | 4.0% | Kimi K2.5 leads |
| AA-Omniscience IndexSource | -8.1% | -34.0% | Kimi K2.5 leads |
| AA-Omniscience AccuracySource | 34.3% | 20.1% | Kimi K2.5 leads |
| AA-Omniscience Hallucination RateSource | 64.6% | 67.8% | Kimi K2.5 leads |
Math9 benchmarks
| Benchmark | Kimi K2.5 | Mistral Large 2 | Result |
|---|---|---|---|
| AIME 2025Source | 96.1% | — | Not comparable |
| AIME26Source | 95.8% | — | Not comparable |
| AIME25 (Arcee)Source | 96.3% | — | Not comparable |
| HMMT Feb 2025Source | 95.4% | — | Not comparable |
| HMMT Nov 2025Source | 91.1% | — | Not comparable |
| HMMT Feb 2026Source | 87.1% | — | Not comparable |
| MMAnswerBenchSource | 81.8% | — | Not comparable |
| FrontierMath v2 (Tiers 1-3)Source | 27.900% | — | Not comparable |
| FrontierMath v2 (Tier 4)Source | 4.200% | — | Not comparable |
Multilingual2 benchmarks
Multimodal6 benchmarks
Frequently Asked Questions (3)
Can I compare Kimi K2.5 and Mistral Large 2 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 K2.5 and Mistral Large 2 today?
Kimi K2.5: $0.60 input / $3.00 output per 1M tokens Mistral Large 2: Pricing unavailable 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.
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