Model comparison
Claude 4 Sonnet vs Kimi K2.5 (Reasoning)
Head-to-head evidence from 15 shared benchmark results across 6 categories. Overall scores shown here use the public BenchAlign v5 ranking lane.
Public leaderboard positions: Claude 4 Sonnet #158 (Supported); Kimi K2.5 (Reasoning) #57 (Estimated). Intervals and evidence labels describe ranking uncertainty, not a guarantee for a specific workload.
Evidence parity. Claude 4 Sonnet and Kimi K2.5 (Reasoning) share 15 comparable benchmark results. 1 of 8 categories are comparable. 1 result is unique to Claude 4 Sonnet; 12 to Kimi K2.5 (Reasoning).
Updated July 20, 2026- Shared results
- 15
- Claude 4 Sonnet only
- 1
- Kimi K2.5 (Reasoning) only
- 12
- Comparable categories
- 1 / 8
Pick Kimi K2.5 (Reasoning) if you want the stronger benchmark profile. Claude 4 Sonnet only becomes the better choice if you need the larger 200K context window or you would rather avoid the extra latency and token burn of a reasoning model.
Confidence note. This is a partial-evidence comparison with 15 shared benchmark results across 6 evidence categories; 1 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 (Reasoning) is clearly ahead on the BenchAlign aggregate, 59.35 to 42.79. The gap is large enough that you do not need to squint at the spreadsheet to see the difference.
Kimi K2.5 (Reasoning)'s sharpest advantage is in coding, where it averages 76.8 against 72.7. The single biggest benchmark swing on the page is SWE-bench Verified, 72.7% to 76.8%.
Claude 4 Sonnet is also the more expensive model on tokens at $3.00 input / $15.00 output per 1M tokens, versus $0.60 input / $3.00 output per 1M tokens for Kimi K2.5 (Reasoning). That is roughly 5.0x on output cost alone. Kimi K2.5 (Reasoning) is the reasoning model in the pair, while Claude 4 Sonnet 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. Claude 4 Sonnet gives you the larger context window at 200K, compared with 128K for Kimi K2.5 (Reasoning).
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 | Claude 4 Sonnet | Δ | Kimi K2.5 (Reasoning) |
|---|---|---|---|
| Coding | Claude 4 Sonnet72.7 | Margin→ 4.1 | Kimi K2.5 (Reasoning)76.8 |
| Agentic | Claude 4 SonnetNot measured | MarginNo overlap | Kimi K2.5 (Reasoning)55.0 |
| Knowledge | Claude 4 SonnetNot measured | MarginNo overlap | Kimi K2.5 (Reasoning)87.2 |
| Multimodal | Claude 4 SonnetNot measured | MarginNo overlap | Kimi K2.5 (Reasoning)78.5 |
Decisive benchmark drivers
The largest measured benchmark gaps in this matchup, with exact reported values.
More
- Source ↗
SWE-bench Verified
CodingA 72.7%B 76.8%Winner: Kimi K2.5 (Reasoning)Δ 4.1SWE-bench Verified: Claude 4 Sonnet scored 72.7%; Kimi K2.5 (Reasoning) scored 76.8%. Kimi K2.5 (Reasoning) wins this benchmark.
Operational comparison
Runtime and commercial metrics are compared only when both models have a complete sourced value.
| Metric | Claude 4 Sonnet | Kimi K2.5 (Reasoning) | Comparison |
|---|---|---|---|
| Input / output priceUSD per 1M tokens | Claude 4 Sonnet$3 input / $15 output | Kimi K2.5 (Reasoning)$0.6 input / $3 output | Kimi K2.5 (Reasoning) has the lower combined listed price. |
| Generation speedtokens per second | Claude 4 Sonnet40 tok/s | Kimi K2.5 (Reasoning)Not available | A complete speed comparison is not available. |
| First-answer latencyseconds to first token | Claude 4 Sonnet1.33 s | Kimi K2.5 (Reasoning)Not available | A complete latency comparison is not available. |
| Context windowmaximum listed tokens | Claude 4 Sonnet200K | Kimi K2.5 (Reasoning)128K | Claude 4 Sonnet lists the larger context window. |
Benchmark Deep Dive
Agentic9 benchmarks
| Benchmark | Claude 4 Sonnet | Kimi K2.5 (Reasoning) | Result |
|---|---|---|---|
| τ²-bench resultsSource | 52.3% | 95.9% | Kimi K2.5 (Reasoning) leads |
| Gert LabsSource | 39.66% | 32.58% | Claude 4 Sonnet leads |
| JobBenchSource | 18.4% | — | Not comparable |
| Terminal-Bench 2.0Source | — | 50.8% | Not comparable |
| BrowseCompSource | — | 60.6% | Not comparable |
| APEX-Agents-AASource | — | 11.5% | Not comparable |
| AA Agentic IndexSource | — | 21.7% | Not comparable |
| GDPval-AASource | — | 25.4% | Not comparable |
| GDPval-AASource | — | 1009 | Not comparable |
CodingKimi K2.5 (Reasoning) wins4 benchmarks
Reasoning2 benchmarks
Knowledge8 benchmarks
| Benchmark | Claude 4 Sonnet | Kimi K2.5 (Reasoning) | Result |
|---|---|---|---|
| Artificial Analysis Intelligence IndexSource | 25.5% | 35.4% | Kimi K2.5 (Reasoning) leads |
| AA-GPQA DiamondSource | 68.3% | 87.9% | Kimi K2.5 (Reasoning) leads |
| AA-HLESource | 4.0% | 29.4% | Kimi K2.5 (Reasoning) leads |
| AA-Omniscience IndexSource | -9.2% | -8.1% | Kimi K2.5 (Reasoning) leads |
| AA-Omniscience AccuracySource | 22.4% | 34.3% | Kimi K2.5 (Reasoning) leads |
| AA-Omniscience Hallucination RateSource | 40.8% | 64.6% | Claude 4 Sonnet leads |
| GPQASource | — | 87.6% | Not comparable |
| MMLU-ProSource | — | 87.1% | Not comparable |
Math1 benchmarks
| Benchmark | Claude 4 Sonnet | Kimi K2.5 (Reasoning) | Result |
|---|---|---|---|
| AIME 2025Source | — | 96.1% | Not comparable |
Multimodal3 benchmarks
Inst. Following1 benchmarks
| Benchmark | Claude 4 Sonnet | Kimi K2.5 (Reasoning) | Result |
|---|---|---|---|
| AA-IFBenchSource | 45.4% | 70.2% | Kimi K2.5 (Reasoning) leads |
Frequently Asked Questions (2)
Which is better, Claude 4 Sonnet or Kimi K2.5 (Reasoning)?
Kimi K2.5 (Reasoning) is ahead on BenchLM's BenchAlign leaderboard, 59.35 to 42.79. The biggest single separator in this matchup is SWE-bench Verified, where the scores are 72.7% and 76.8%.
Which is better for coding, Claude 4 Sonnet or Kimi K2.5 (Reasoning)?
Kimi K2.5 (Reasoning) has the edge for coding in this comparison, averaging 76.8 versus 72.7. Inside this category, AA-SciCode is the benchmark that creates the most daylight between them.
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