Model comparison
Claude 3.5 Haiku vs GPT-5.4
GPT-5.4 is the stronger model overall, scoring 59.4 to 29.2 on the Noometry Index.
Last verified . 27 shared benchmarks.
Summary
- They share 27 benchmarks with published results for both. Claude 3.5 Haiku scores higher in 0 categories and GPT-5.4 in 10 categories; 10 gaps are clear of the uncertainty.
- The widest gap is in math, where GPT-5.4 leads 73.5 to 14.7.
- The biggest single-benchmark swing is OTIS Mock AIME 2024-2025: 4.3% for Claude 3.5 Haiku and 97.8% for GPT-5.4.
Side by side
| Claude 3.5 Haiku | GPT-5.4 | |
|---|---|---|
| Provider | Anthropic | OpenAI |
| Noometry Index | 29.2 | 59.4 |
| Released | 2024-10-22 | 2026-03-05 |
| Weights | Proprietary | Proprietary |
| Context window | — | 1.05M |
| Max output | — | 128K |
| Input $ / M tokens | — | $2.50 |
| Output $ / M tokens | — | $15 |
| Results tracked | 49 | 68 |
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Category by category
Coding GPT-5.4 leads
Claude 3.5 Haiku: 32.9 (#265), GPT-5.4: 52.6 (#33)
| Benchmark | Claude 3.5 Haiku | GPT-5.4 |
|---|---|---|
| SciCode | 27.4% | 56.6% |
| WeirdML | 30.7% | 77.7% |
| LMArena Coding | 1286 | 1497 |
| SWE-bench Verified | — | 76.9% |
| DeepSWE | — | 51.8% |
| Aider Polyglot | 28% | — |
| LMArena WebDev | — | 1465 |
| GSO | — | 31.4% |
| BigCodeBench Instruct | 46.1% | — |
| LiveBench Coding | 51.4% | — |
| MirrorCode | — | 15.6% |
| BigCodeBench Complete | 59% | — |
| CadEval | 32% | — |
| ALE-Bench | — | 1,607 |
| AlgoTune | — | 1.85 |
Agentic & Tool Use GPT-5.4 leads
Claude 3.5 Haiku: 28.0 (#95), GPT-5.4: 46.5 (#13)
| Benchmark | Claude 3.5 Haiku | GPT-5.4 |
|---|---|---|
| Terminal-Bench | — | 81.8% |
| APEX-Agents | — | 52.4% |
| τ²-bench Banking | — | 39.4% |
| DeepResearch Bench | — | 35.1% |
| PostTrainBench | — | 19% |
| BALROG | 19.3% | — |
| GBAEval | — | 45.1% |
| LMArena Search | — | 1197 |
| METR Time Horizons | — | 74.3% |
| Vending-Bench 2 | — | 6,144 |
Reasoning GPT-5.4 leads
Claude 3.5 Haiku: 17.7 (#290), GPT-5.4: 61.8 (#19)
| Benchmark | Claude 3.5 Haiku | GPT-5.4 |
|---|---|---|
| CritPt | 0% | 23.4% |
| LMArena Hard Prompts | 1251 | 1485 |
| DTBench | 56.7% | 94.4% |
| Epoch Capabilities Index | 127.15 | 156.81 |
| ARC-AGI-2 | — | 74% |
| Kagi LLM Benchmark | — | 63.8% |
| NYT Connections (extended) | — | 91.3% |
| ARC-AGI-1 | — | 93.7% |
| Chess Puzzles | — | 44% |
| EnigmaEval | — | 16% |
| Thematic Generalization | — | 80% |
| EBR-Bench | — | 25.4% |
| LiveBench Reasoning | 28.1% | — |
| Mystery Game Puzzles | — | 37% |
| LiveBench Data Analysis | 48.5% | — |
| LMCA | — | 52% |
| ForecastBench | — | 59.5 |
| LiveBench | 43.5% | — |
Math GPT-5.4 leads
Claude 3.5 Haiku: 14.7 (#300), GPT-5.4: 73.5 (#19)
| Benchmark | Claude 3.5 Haiku | GPT-5.4 |
|---|---|---|
| OTIS Mock AIME 2024-2025 | 4.3% | 97.8% |
| LMArena Math | 1244 | 1488 |
| FrontierMath (Feb 2025 set) | 0.3% | 47.6% |
| FrontierMath (Tiers 1-3) | — | 78.6% |
| FrontierMath Tier 4 | — | 49% |
| MathArena Final-Answer Competitions | — | 83.1% |
| ProofBench | — | 56% |
| Omni-MATH | 22.4% | — |
| LiveBench Math | 35.5% | — |
| MATH Level 5 | 46.4% | — |
| FrontierMath Tier 4 (v1) | — | 27.1% |
Knowledge GPT-5.4 leads
Claude 3.5 Haiku: 18.7 (#281), GPT-5.4: 65.3 (#14)
| Benchmark | Claude 3.5 Haiku | GPT-5.4 |
|---|---|---|
| GPQA Diamond | 38.1% | 93.3% |
| LMArena Expert | 1208 | 1507 |
| Humanity's Last Exam | — | 36.2% |
| SimpleQA Verified | — | 45.1% |
| MMLU-Pro | 60.5% | — |
| Confabulations | 36.7% | — |
| Vectara Hallucination Rate | — | 7% |
| GPQA (HELM) | 36.3% | — |
| MMLU | 74.3% | — |
Multimodal GPT-5.4 leads
Claude 3.5 Haiku: 26.8 (#117), GPT-5.4: 43.7 (#20)
| Benchmark | Claude 3.5 Haiku | GPT-5.4 |
|---|---|---|
| LMArena Vision | 1092 | 1303 |
| GeoBench | 34% | — |
| Blueprint-Bench 2 | — | 27.1% |
| Furniture Assembly | — | 37.5% |
| LMArena Document | — | 1471 |
Multilingual GPT-5.4 leads
Claude 3.5 Haiku: 40.0 (#218), GPT-5.4: 56.2 (#23)
| Benchmark | Claude 3.5 Haiku | GPT-5.4 |
|---|---|---|
| LMArena Non-English | 1238 | 1465 |
| LMArena Chinese | 1229 | 1519 |
| LMArena French | 1264 | 1493 |
| LMArena German | 1237 | 1472 |
| LMArena Japanese | 1175 | 1485 |
| LMArena Korean | 1173 | 1448 |
| LMArena Russian | 1253 | 1480 |
| LMArena Spanish | 1261 | 1454 |
Instruction Following GPT-5.4 leads
Claude 3.5 Haiku: 62.9 (#234), GPT-5.4: 77.1 (#27)
| Benchmark | Claude 3.5 Haiku | GPT-5.4 |
|---|---|---|
| LMArena Instruction Following | 1241 | 1469 |
| LiveBench Instruction Following | 61.9% | — |
| IFEval | 79.2% | — |
Long Context GPT-5.4 leads
Claude 3.5 Haiku: 38.3 (#200), GPT-5.4: 50.3 (#8)
| Benchmark | Claude 3.5 Haiku | GPT-5.4 |
|---|---|---|
| LMArena Longer Query | 1261 | 1473 |
| CL-bench | — | 27.9% |
| CL-bench Life | — | 21.7% |
Writing & Preference GPT-5.4 leads
Claude 3.5 Haiku: 42.7 (#234), GPT-5.4: 71.9 (#17)
| Benchmark | Claude 3.5 Haiku | GPT-5.4 |
|---|---|---|
| LMArena Text | 1255 | 1469 |
| LMArena Creative Writing | 1233 | 1439 |
| EQ-Bench Creative Writing | 1146 | 1840 |
| LMArena Multi-Turn | 1265 | 1482 |
| Short-Story Creative Writing | 73.5% | — |
| WildBench | 76% | — |
| EQ-Bench 4 | — | 1272 |
| LiveBench Language | 35.4% | — |
Frequently asked questions
Is Claude 3.5 Haiku better than GPT-5.4?
GPT-5.4 is the stronger model overall, scoring 59.4 to 29.2 on the Noometry Index.
Is Claude 3.5 Haiku or GPT-5.4 better for coding?
GPT-5.4 scores higher on coding benchmarks: 52.6 versus 32.9 in the Noometry coding category.
How many benchmarks do Claude 3.5 Haiku and GPT-5.4 share?
27 benchmarks have published results for both models. Claude 3.5 Haiku has 49 scored results on Noometry and GPT-5.4 has 68.