Model comparison
Claude Haiku 4.5 vs GPT-5.3 Chat
GPT-5.3 Chat is the stronger model overall, scoring 42.8 to 39.5 on the Noometry Index. Claude Haiku 4.5 costs 2.4× less per token, which makes it the better buy when GPT-5.3 Chat's lead doesn't matter for your workload.
Last verified . 17 shared benchmarks.
Summary
- They share 17 benchmarks with published results for both. Claude Haiku 4.5 scores higher in 3 categories and GPT-5.3 Chat in 5 categories; 7 gaps are clear of the uncertainty.
- The widest gap is in reasoning, where GPT-5.3 Chat leads 28.5 to 15.1.
- Claude Haiku 4.5 is cheaper at $1 / $5 per million input/output tokens, against $1.75 / $14 for GPT-5.3 Chat.
- Claude Haiku 4.5 accepts more context: 200K tokens versus 128K.
Side by side
| Claude Haiku 4.5 | GPT-5.3 Chat | |
|---|---|---|
| Provider | Anthropic | OpenAI |
| Noometry Index | 39.5 | 42.8 |
| Released | 2025-10-15 | 2026-03-03 |
| Weights | Proprietary | Proprietary |
| Context window | 200K | 128K |
| Max output | 64K | 16K |
| Input $ / M tokens | $1 | $1.75 |
| Output $ / M tokens | $5 | $14 |
| Results tracked | 53 | 18 |
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Category by category
Coding Claude Haiku 4.5 leads
Claude Haiku 4.5: 44.0 (#78), GPT-5.3 Chat: 41.4 (#124)
| Benchmark | Claude Haiku 4.5 | GPT-5.3 Chat |
|---|---|---|
| LMArena Coding | 1453 | 1408 |
| 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: 33.6 (#52), GPT-5.3 Chat: —
| Benchmark | Claude Haiku 4.5 | GPT-5.3 Chat |
|---|---|---|
| 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 GPT-5.3 Chat leads
Claude Haiku 4.5: 15.1 (#320), GPT-5.3 Chat: 28.5 (#102)
| Benchmark | Claude Haiku 4.5 | GPT-5.3 Chat |
|---|---|---|
| LMArena Hard Prompts | 1420 | 1399 |
| 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 | 142.41 | — |
| ForecastBench | 61.4 | — |
Math Claude Haiku 4.5 leads
Claude Haiku 4.5: 44.9 (#78), GPT-5.3 Chat: 38.2 (#142)
| Benchmark | Claude Haiku 4.5 | GPT-5.3 Chat |
|---|---|---|
| LMArena Math | 1396 | 1389 |
| 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 GPT-5.3 Chat leads
Claude Haiku 4.5: 37.7 (#153), GPT-5.3 Chat: 38.8 (#140)
| Benchmark | Claude Haiku 4.5 | GPT-5.3 Chat |
|---|---|---|
| LMArena Expert | 1442 | 1397 |
| GPQA Diamond | 71.2% | — |
| SimpleQA Verified | 13.2% | — |
| MMLU-Pro | 77.7% | — |
| Vectara Hallucination Rate | 9.8% | — |
| GPQA (HELM) | 60.5% | — |
Multimodal Not comparable
Claude Haiku 4.5: 26.8 (#118), GPT-5.3 Chat: —
| Benchmark | Claude Haiku 4.5 | GPT-5.3 Chat |
|---|---|---|
| Blueprint-Bench 2 | 0% | — |
| LMArena Document | 1420 | — |
Multilingual Too close to call
Claude Haiku 4.5: 49.9 (#129), GPT-5.3 Chat: 50.3 (#124)
| Benchmark | Claude Haiku 4.5 | GPT-5.3 Chat |
|---|---|---|
| LMArena Non-English | 1377 | 1382 |
| LMArena Chinese | 1417 | 1432 |
| LMArena French | 1408 | 1397 |
| LMArena German | 1375 | 1384 |
| LMArena Japanese | 1339 | 1352 |
| LMArena Korean | 1347 | 1346 |
| LMArena Russian | 1381 | 1400 |
| LMArena Spanish | 1420 | 1371 |
Instruction Following GPT-5.3 Chat leads
Claude Haiku 4.5: 71.4 (#149), GPT-5.3 Chat: 72.8 (#129)
| Benchmark | Claude Haiku 4.5 | GPT-5.3 Chat |
|---|---|---|
| LMArena Instruction Following | 1414 | 1378 |
| IFEval | 80.1% | — |
Long Context Claude Haiku 4.5 leads
Claude Haiku 4.5: 43.6 (#92), GPT-5.3 Chat: 42.6 (#120)
| Benchmark | Claude Haiku 4.5 | GPT-5.3 Chat |
|---|---|---|
| LMArena Longer Query | 1427 | 1396 |
Writing & Preference GPT-5.3 Chat leads
Claude Haiku 4.5: 57.9 (#123), GPT-5.3 Chat: 63.1 (#68)
| Benchmark | Claude Haiku 4.5 | GPT-5.3 Chat |
|---|---|---|
| LMArena Text | 1396 | 1389 |
| LMArena Creative Writing | 1372 | 1355 |
| LMArena Multi-Turn | 1409 | 1412 |
| EQ-Bench Creative Writing | — | 1690 |
| WildBench | 83.9% | — |
| EQ-Bench 4 | 1064 | — |
Frequently asked questions
Is Claude Haiku 4.5 better than GPT-5.3 Chat?
GPT-5.3 Chat is the stronger model overall, scoring 42.8 to 39.5 on the Noometry Index. Claude Haiku 4.5 costs 2.4× less per token, which makes it the better buy when GPT-5.3 Chat's lead doesn't matter for your workload.
Which is cheaper, Claude Haiku 4.5 or GPT-5.3 Chat?
Claude Haiku 4.5 is cheaper. It lists at $1 per million input tokens and $5 per million output tokens; GPT-5.3 Chat lists at $1.75 and $14.
Is Claude Haiku 4.5 or GPT-5.3 Chat better for coding?
Claude Haiku 4.5 scores higher on coding benchmarks: 44.0 versus 41.4 in the Noometry coding category.
Which has the bigger context window?
Claude Haiku 4.5 does, with 200K tokens against 128K.
How many benchmarks do Claude Haiku 4.5 and GPT-5.3 Chat share?
17 benchmarks have published results for both models. Claude Haiku 4.5 has 53 scored results on Noometry and GPT-5.3 Chat has 18.