Model comparison
Claude Haiku 4.5 vs Kimi K2.7 Code
Kimi K2.7 Code is the stronger model overall, scoring 43.3 to 39.5 on the Noometry Index.
Last verified . 11 shared benchmarks.
Summary
- They share 11 benchmarks with published results for both. Claude Haiku 4.5 scores higher in 2 categories and Kimi K2.7 Code in 3 categories; 5 gaps are clear of the uncertainty.
- The widest gap is in reasoning, where Kimi K2.7 Code leads 39.0 to 15.1.
- The biggest single-benchmark swing is OTIS Mock AIME 2024-2025: 66.7% for Claude Haiku 4.5 and 95.6% for Kimi K2.7 Code.
- Kimi K2.7 Code is cheaper at $0.95 / $4 per million input/output tokens, against $1 / $5 for Claude Haiku 4.5.
- Kimi K2.7 Code accepts more context: 262K tokens versus 200K.
- Kimi K2.7 Code has downloadable open weights; the other is API-only.
Side by side
| Claude Haiku 4.5 | Kimi K2.7 Code | |
|---|---|---|
| Provider | Anthropic | Moonshot AI |
| Noometry Index | 39.5 | 43.3 |
| Released | 2025-10-15 | 2026-06-12 |
| Weights | Proprietary | Open |
| Context window | 200K | 262K |
| Max output | 64K | 262K |
| Input $ / M tokens | $1 | $0.95 |
| Output $ / M tokens | $5 | $4 |
| Results tracked | 53 | 19 |
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Category by category
Coding Claude Haiku 4.5 leads
Claude Haiku 4.5: 44.0 (#78), Kimi K2.7 Code: 42.9 (#95)
| Benchmark | Claude Haiku 4.5 | Kimi K2.7 Code |
|---|---|---|
| LMArena WebDev | 1330 | 1473 |
| SciCode | 43.3% | 47.5% |
| WeirdML | 45.4% | 54.1% |
| ALE-Bench | 653.48 | 886.23 |
| DeepSWE | — | 30.5% |
| FrontierCode | — | 30.1% |
| SWE-bench Verified (bash only) | 66.6% | — |
| SWE-bench Multilingual | 64.7% | — |
| LMArena Coding | 1453 | — |
Agentic & Tool Use Claude Haiku 4.5 leads
Claude Haiku 4.5: 33.6 (#52), Kimi K2.7 Code: 24.0 (#122)
| Benchmark | Claude Haiku 4.5 | Kimi K2.7 Code |
|---|---|---|
| Vending-Bench 2 | 458.89 | 5,083 |
| Terminal-Bench | 35.5% | — |
| APEX-Agents | — | 37.6% |
| Berkeley Function Calling Leaderboard | 68.7% | — |
| DeepResearch Bench | 45.5% | — |
| BALROG | 31.2% | — |
| ExploitBench | 13.7% | — |
| GBAEval | — | 0.9% |
Reasoning Kimi K2.7 Code leads
Claude Haiku 4.5: 15.1 (#320), Kimi K2.7 Code: 39.0 (#61)
| Benchmark | Claude Haiku 4.5 | Kimi K2.7 Code |
|---|---|---|
| CritPt | 0% | 10% |
| Chess Puzzles | 8% | 21% |
| Epoch Capabilities Index | 142.41 | 149.97 |
| ARC-AGI-2 | 4% | — |
| SimpleBench | — | 57.9% |
| NYT Connections (extended) | 14.3% | — |
| ARC-AGI-1 | 47.7% | — |
| LMArena Hard Prompts | 1420 | — |
| DTBench | 73.6% | — |
| LMCA | 30.9% | — |
| Surface Evolver Bench | — | 48.8% |
| ForecastBench | 61.4 | — |
Math Kimi K2.7 Code leads
Claude Haiku 4.5: 44.9 (#78), Kimi K2.7 Code: 52.9 (#48)
| Benchmark | Claude Haiku 4.5 | Kimi K2.7 Code |
|---|---|---|
| OTIS Mock AIME 2024-2025 | 66.7% | 95.6% |
| FrontierMath (Tiers 1-3) | — | 54% |
| FrontierMath Tier 4 | — | 12.2% |
| Omni-MATH | 56.1% | — |
| LMArena Math | 1396 | — |
| MATH Level 5 | 96.4% | — |
| FrontierMath (Feb 2025 set) | 5.9% | — |
| FrontierMath Tier 4 (v1) | 2.1% | — |
Knowledge Kimi K2.7 Code leads
Claude Haiku 4.5: 37.7 (#153), Kimi K2.7 Code: 53.5 (#57)
| Benchmark | Claude Haiku 4.5 | Kimi K2.7 Code |
|---|---|---|
| GPQA Diamond | 71.2% | 87.9% |
| SimpleQA Verified | 13.2% | 36.5% |
| MMLU-Pro | 77.7% | — |
| Vectara Hallucination Rate | 9.8% | — |
| GPQA (HELM) | 60.5% | — |
| LMArena Expert | 1442 | — |
Multimodal Not comparable
Claude Haiku 4.5: 26.8 (#118), Kimi K2.7 Code: —
| Benchmark | Claude Haiku 4.5 | Kimi K2.7 Code |
|---|---|---|
| Blueprint-Bench 2 | 0% | — |
| LMArena Document | 1420 | — |
Multilingual Not comparable
Claude Haiku 4.5: 49.9 (#129), Kimi K2.7 Code: —
| Benchmark | Claude Haiku 4.5 | Kimi K2.7 Code |
|---|---|---|
| LMArena Non-English | 1377 | — |
| LMArena Chinese | 1417 | — |
| LMArena French | 1408 | — |
| LMArena German | 1375 | — |
| LMArena Japanese | 1339 | — |
| LMArena Korean | 1347 | — |
| LMArena Russian | 1381 | — |
| LMArena Spanish | 1420 | — |
Instruction Following Not comparable
Claude Haiku 4.5: 71.4 (#149), Kimi K2.7 Code: —
| Benchmark | Claude Haiku 4.5 | Kimi K2.7 Code |
|---|---|---|
| IFEval | 80.1% | — |
| LMArena Instruction Following | 1414 | — |
Long Context Not comparable
Claude Haiku 4.5: 43.6 (#92), Kimi K2.7 Code: —
| Benchmark | Claude Haiku 4.5 | Kimi K2.7 Code |
|---|---|---|
| LMArena Longer Query | 1427 | — |
Writing & Preference Not comparable
Claude Haiku 4.5: 57.9 (#123), Kimi K2.7 Code: —
| Benchmark | Claude Haiku 4.5 | Kimi K2.7 Code |
|---|---|---|
| LMArena Text | 1396 | — |
| LMArena Creative Writing | 1372 | — |
| WildBench | 83.9% | — |
| EQ-Bench 4 | 1064 | — |
| LMArena Multi-Turn | 1409 | — |
Frequently asked questions
Is Claude Haiku 4.5 better than Kimi K2.7 Code?
Kimi K2.7 Code is the stronger model overall, scoring 43.3 to 39.5 on the Noometry Index.
Which is cheaper, Claude Haiku 4.5 or Kimi K2.7 Code?
Kimi K2.7 Code is cheaper. It lists at $0.95 per million input tokens and $4 per million output tokens; Claude Haiku 4.5 lists at $1 and $5.
Is Claude Haiku 4.5 or Kimi K2.7 Code better for coding?
Claude Haiku 4.5 scores higher on coding benchmarks: 44.0 versus 42.9 in the Noometry coding category.
Which has the bigger context window?
Kimi K2.7 Code does, with 262K tokens against 200K.
How many benchmarks do Claude Haiku 4.5 and Kimi K2.7 Code share?
11 benchmarks have published results for both models. Claude Haiku 4.5 has 53 scored results on Noometry and Kimi K2.7 Code has 19.