Model comparison
Claude Haiku 4.5 vs MiMo-V2-Omni
MiMo-V2-Omni is the stronger model overall, scoring 42.9 to 39.3 on the Noometry Index.
Last verified . 17 shared benchmarks.
Summary
- They share 17 benchmarks with published results for both. Claude Haiku 4.5 scores higher in 2 categories and MiMo-V2-Omni in 7 categories; 8 gaps are clear of the uncertainty.
- The widest gap is in reasoning, where MiMo-V2-Omni leads 28.6 to 15.1.
- MiMo-V2-Omni is cheaper at $0.14 / $0.28 per million input/output tokens, against $1 / $5 for Claude Haiku 4.5.
- MiMo-V2-Omni accepts more context: 262K tokens versus 200K.
Side by side
| Claude Haiku 4.5 | MiMo-V2-Omni | |
|---|---|---|
| Provider | Anthropic | Xiaomi |
| Noometry Index | 39.3 | 42.9 |
| Released | 2025-10-15 | 2026-03-18 |
| Weights | Proprietary | Proprietary |
| Context window | 200K | 262K |
| Max output | 64K | 131K |
| Input $ / M tokens | $1 | $0.14 |
| Output $ / M tokens | $5 | $0.28 |
| Results tracked | 53 | 18 |
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Category by category
Coding Claude Haiku 4.5 leads
Claude Haiku 4.5: 43.9 (#71), MiMo-V2-Omni: 42.7 (#87)
| Benchmark | Claude Haiku 4.5 | MiMo-V2-Omni |
|---|---|---|
| LMArena Coding | 1454 | 1467 |
| SWE-bench Verified (bash only) | 66.6% | — |
| LMArena WebDev | 1330 | — |
| SWE-bench Multilingual | 64.7% | — |
| SciCode | 43.3% | — |
| WeirdML | 45.4% | — |
| ALE-Bench | 653.48 | — |
Agentic & Tool Use Not comparable
Claude Haiku 4.5: 33.5 (#50), MiMo-V2-Omni: —
| Benchmark | Claude Haiku 4.5 | MiMo-V2-Omni |
|---|---|---|
| Terminal-Bench | 35.5% | — |
| Berkeley Function Calling Leaderboard | 68.7% | — |
| DeepResearch Bench | 45.5% | — |
| BALROG | 31.2% | — |
| ExploitBench | 13.7% | — |
| Vending-Bench 2 | 458.89 | — |
Reasoning MiMo-V2-Omni leads
Claude Haiku 4.5: 15.1 (#310), MiMo-V2-Omni: 28.6 (#86)
| Benchmark | Claude Haiku 4.5 | MiMo-V2-Omni |
|---|---|---|
| LMArena Hard Prompts | 1420 | 1445 |
| ARC-AGI-2 | 4% | — |
| NYT Connections (extended) | 14.3% | — |
| ARC-AGI-1 | 47.7% | — |
| CritPt | 0% | — |
| Chess Puzzles | 8% | — |
| DTBench | 73.6% | — |
| LMCA | 30.9% | — |
| Epoch Capabilities Index | 142.42 | — |
| ForecastBench | 61.4 | — |
Math Claude Haiku 4.5 leads
Claude Haiku 4.5: 43.0 (#69), MiMo-V2-Omni: 36.4 (#112)
| Benchmark | Claude Haiku 4.5 | MiMo-V2-Omni |
|---|---|---|
| LMArena Math | 1395 | 1432 |
| OTIS Mock AIME 2024-2025 | 66.7% | — |
| Omni-MATH | 56.1% | — |
| MATH Level 5 | 96.4% | — |
| FrontierMath (Feb 2025 set) | 5.9% | — |
| FrontierMath Tier 4 (v1) | 2.1% | — |
Knowledge MiMo-V2-Omni leads
Claude Haiku 4.5: 37.6 (#146), MiMo-V2-Omni: 39.9 (#114)
| Benchmark | Claude Haiku 4.5 | MiMo-V2-Omni |
|---|---|---|
| LMArena Expert | 1443 | 1449 |
| GPQA Diamond | 71.2% | — |
| SimpleQA Verified | 13.2% | — |
| MMLU-Pro | 77.7% | — |
| Vectara Hallucination Rate | 9.8% | — |
| GPQA (HELM) | 60.5% | — |
Multimodal MiMo-V2-Omni leads
Claude Haiku 4.5: 27.0 (#111), MiMo-V2-Omni: 37.9 (#62)
| Benchmark | Claude Haiku 4.5 | MiMo-V2-Omni |
|---|---|---|
| LMArena Vision | — | 1228 |
| Blueprint-Bench 2 | 0% | — |
| LMArena Document | 1420 | — |
Multilingual MiMo-V2-Omni leads
Claude Haiku 4.5: 50.1 (#124), MiMo-V2-Omni: 52.0 (#97)
| Benchmark | Claude Haiku 4.5 | MiMo-V2-Omni |
|---|---|---|
| LMArena Non-English | 1376 | 1404 |
| LMArena Chinese | 1415 | 1469 |
| LMArena French | 1408 | 1448 |
| LMArena German | 1377 | 1398 |
| LMArena Japanese | 1339 | 1318 |
| LMArena Korean | 1346 | 1355 |
| LMArena Russian | 1380 | 1412 |
| LMArena Spanish | 1420 | 1432 |
Instruction Following MiMo-V2-Omni leads
Claude Haiku 4.5: 71.6 (#144), MiMo-V2-Omni: 75.4 (#62)
| Benchmark | Claude Haiku 4.5 | MiMo-V2-Omni |
|---|---|---|
| LMArena Instruction Following | 1414 | 1428 |
| IFEval | 80.1% | — |
Long Context Too close to call
Claude Haiku 4.5: 43.6 (#86), MiMo-V2-Omni: 44.1 (#72)
| Benchmark | Claude Haiku 4.5 | MiMo-V2-Omni |
|---|---|---|
| LMArena Longer Query | 1427 | 1442 |
Writing & Preference MiMo-V2-Omni leads
Claude Haiku 4.5: 58.1 (#119), MiMo-V2-Omni: 61.6 (#83)
| Benchmark | Claude Haiku 4.5 | MiMo-V2-Omni |
|---|---|---|
| LMArena Text | 1396 | 1424 |
| LMArena Creative Writing | 1371 | 1393 |
| LMArena Multi-Turn | 1409 | 1446 |
| WildBench | 83.9% | — |
| EQ-Bench 4 | 1064 | — |
Frequently asked questions
Is Claude Haiku 4.5 better than MiMo-V2-Omni?
MiMo-V2-Omni is the stronger model overall, scoring 42.9 to 39.3 on the Noometry Index.
Which is cheaper, Claude Haiku 4.5 or MiMo-V2-Omni?
MiMo-V2-Omni is cheaper. It lists at $0.14 per million input tokens and $0.28 per million output tokens; Claude Haiku 4.5 lists at $1 and $5.
Is Claude Haiku 4.5 or MiMo-V2-Omni better for coding?
Claude Haiku 4.5 scores higher on coding benchmarks: 43.9 versus 42.7 in the Noometry coding category.
Which has the bigger context window?
MiMo-V2-Omni does, with 262K tokens against 200K.
How many benchmarks do Claude Haiku 4.5 and MiMo-V2-Omni share?
17 benchmarks have published results for both models. Claude Haiku 4.5 has 53 scored results on Noometry and MiMo-V2-Omni has 18.