Model comparison
Kimi K2.5 vs MiMo-V2-Flash
Head-to-head evidence from 19 shared benchmark results across 6 categories. Overall scores shown here use the public BenchAlign v5 ranking lane.
Public leaderboard positions: Kimi K2.5 #54 (Supported); MiMo-V2-Flash #91 (Supported). Intervals and evidence labels describe ranking uncertainty, not a guarantee for a specific workload.
Evidence parity. Kimi K2.5 and MiMo-V2-Flash share 19 comparable benchmark results. 2 of 8 categories are comparable. 44 results are unique to Kimi K2.5; 0 to MiMo-V2-Flash.
Updated July 20, 2026- Shared results
- 19
- Kimi K2.5 only
- 44
- MiMo-V2-Flash only
- 0
- Comparable categories
- 2 / 8
Pick Kimi K2.5 if you want the stronger benchmark profile. MiMo-V2-Flash only becomes the better choice if knowledge is the priority or you want the cheaper token bill.
Confidence note. This is a partial-evidence comparison with 19 shared benchmark results across 6 evidence categories; 2 of 8 categories currently have scoreable aggregates for both models. Treat the verdict as directional until coverage is more balanced.
Why this result
Kimi K2.5 is clearly ahead on the BenchAlign aggregate, 59.66 to 54.06. The gap is large enough that you do not need to squint at the spreadsheet to see the difference.
Kimi K2.5 is also the more expensive model on tokens at $0.60 input / $3.00 output per 1M tokens, versus $0.00 input / $0.00 output per 1M tokens for MiMo-V2-Flash. That is roughly Infinityx on output cost alone. MiMo-V2-Flash is the reasoning model in the pair, while Kimi K2.5 is not. That usually helps on harder chain-of-thought-heavy tests, but it can also mean more latency and more token spend in real use.
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 | MiMo-V2-Flash |
|---|---|---|---|
| Knowledge | Kimi K2.556.9 | Margin→ 27.8 | MiMo-V2-Flash84.7 |
| Coding | Kimi K2.559.4 | Margin→ 14.0 | MiMo-V2-Flash73.4 |
| Agentic | Kimi K2.555.0 | MarginNo overlap | MiMo-V2-FlashNot measured |
| Reasoning | Kimi K2.561.0 | MarginNo overlap | MiMo-V2-FlashNot measured |
| Math | Kimi K2.560.6 | MarginNo overlap | MiMo-V2-FlashNot measured |
| Multilingual | Kimi K2.582.3 | MarginNo overlap | MiMo-V2-FlashNot measured |
| Multimodal | Kimi K2.578.5 | MarginNo overlap | MiMo-V2-FlashNot measured |
| Inst. Following | Kimi K2.593.9 | MarginNo overlap | MiMo-V2-FlashNot measured |
Decisive benchmark drivers
The largest measured benchmark gaps in this matchup, with exact reported values.
More
- Source ↗
GPQA
KnowledgeA 87.6%B 83.7%Winner: Kimi K2.5Δ 3.9GPQA: Kimi K2.5 scored 87.6%; MiMo-V2-Flash scored 83.7%. Kimi K2.5 wins this benchmark. - Source ↗
SWE-bench Verified
CodingA 76.8%B 73.4%Winner: Kimi K2.5Δ 3.4SWE-bench Verified: Kimi K2.5 scored 76.8%; MiMo-V2-Flash scored 73.4%. Kimi K2.5 wins this benchmark. - Source ↗
MMLU-Pro
KnowledgeA 87.1%B 84.9%Winner: Kimi K2.5Δ 2.2MMLU-Pro: Kimi K2.5 scored 87.1%; MiMo-V2-Flash scored 84.9%. Kimi K2.5 wins this benchmark.
Operational comparison
Runtime and commercial metrics are compared only when both models have a complete sourced value.
| Metric | Kimi K2.5 | MiMo-V2-Flash | Comparison |
|---|---|---|---|
| Input / output priceUSD per 1M tokens | Kimi K2.5$0.6 input / $3 output | MiMo-V2-Flash$0 input / $0 output | MiMo-V2-Flash has the lower combined listed price. |
| Generation speedtokens per second | Kimi K2.545 tok/s | MiMo-V2-Flash129 tok/s | MiMo-V2-Flash has the higher measured throughput. |
| First-answer latencyseconds to first token | Kimi K2.52.38 s | MiMo-V2-Flash2.14 s | MiMo-V2-Flash reaches the first token sooner. |
| Context windowmaximum listed tokens | Kimi K2.5256K | MiMo-V2-Flash256K | Listed context windows are equal. |
Benchmark Deep Dive
Agentic19 benchmarks
| Benchmark | Kimi K2.5 | MiMo-V2-Flash | 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% | 83.9% | 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% | 12.0% | Kimi K2.5 leads |
| GDPval-AASource | 25.4% | 16.7% | Kimi K2.5 leads |
| GDPval-AASource | 1009 | 833 | Kimi K2.5 leads |
CodingMiMo-V2-Flash wins10 benchmarks
| Benchmark | Kimi K2.5 | MiMo-V2-Flash | Result |
|---|---|---|---|
| SWE-bench VerifiedSource | 76.8% | 73.4% | Kimi K2.5 leads |
| 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% | 25.9% | Kimi K2.5 leads |
| AA Coding IndexSource | 46.8% | 49.8% | MiMo-V2-Flash leads |
Reasoning3 benchmarks
KnowledgeMiMo-V2-Flash wins12 benchmarks
| Benchmark | Kimi K2.5 | MiMo-V2-Flash | Result |
|---|---|---|---|
| GPQASource | 87.6% | 83.7% | Kimi K2.5 leads |
| GPQA-DSource | 87.6% | — | Not comparable |
| SuperGPQASource | 69.2% | — | Not comparable |
| MMLU-ProSource | 87.1% | 84.9% | Kimi K2.5 leads |
| MMLU-Pro (Arcee)Source | 87.1% | — | Not comparable |
| HLESource | 30.1% | — | Not comparable |
| Artificial Analysis Intelligence IndexSource | 35.4% | 24.7% | Kimi K2.5 leads |
| AA-GPQA DiamondSource | 87.9% | 65.6% | Kimi K2.5 leads |
| AA-HLESource | 29.4% | 8.0% | Kimi K2.5 leads |
| AA-Omniscience IndexSource | -8.1% | -48.5% | Kimi K2.5 leads |
| AA-Omniscience AccuracySource | 34.3% | 15.2% | Kimi K2.5 leads |
| AA-Omniscience Hallucination RateSource | 64.6% | 75.1% | Kimi K2.5 leads |
Math9 benchmarks
| Benchmark | Kimi K2.5 | MiMo-V2-Flash | Result |
|---|---|---|---|
| AIME 2025Source | 96.1% | 94.1% | Kimi K2.5 leads |
| 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)
Which is better, Kimi K2.5 or MiMo-V2-Flash?
Kimi K2.5 is ahead on BenchLM's BenchAlign leaderboard, 59.66 to 54.06. The biggest single separator in this matchup is GPQA, where the scores are 87.6% and 83.7%.
Which is better for knowledge tasks, Kimi K2.5 or MiMo-V2-Flash?
MiMo-V2-Flash has the edge for knowledge tasks in this comparison, averaging 84.7 versus 56.9. Inside this category, AA-Omniscience Index is the benchmark that creates the most daylight between them.
Which is better for coding, Kimi K2.5 or MiMo-V2-Flash?
MiMo-V2-Flash has the edge for coding in this comparison, averaging 73.4 versus 59.4. Inside this category, AA-SciCode is the benchmark that creates the most daylight between them.
Self-host vs API cost
Estimates at 50,000 req/day · 1000 tokens/req average.
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