# Claude Haiku 4.5 vs GPT-5.3 Codex

> GPT-5.3 Codex is the stronger model overall, scoring 45.5 to 39.3 on the Noometry Index. Claude Haiku 4.5 costs 2.4× less per token, which makes it the better buy when GPT-5.3 Codex's lead doesn't matter for your workload.

- Canonical page: https://noometry.com/compare/claude-haiku-4-5-vs-gpt-5-3-codex
- Last updated: 2026-10-10
- Shared benchmarks: 6

## Summary

- They share 6 benchmarks with published results for both. Claude Haiku 4.5 scores higher in 0 categories and GPT-5.3 Codex in 2 categories; 2 gaps are clear of the uncertainty.
- The widest gap is in agentic & tool use, where GPT-5.3 Codex leads 47.8 to 33.5.
- The biggest single-benchmark swing is Terminal-Bench: 35.5% for Claude Haiku 4.5 and 78.4% for GPT-5.3 Codex.
- Claude Haiku 4.5 is cheaper at $1 / $5 per million input/output tokens, against $1.75 / $14 for GPT-5.3 Codex.
- GPT-5.3 Codex accepts more context: 400K tokens versus 200K.

## Snapshot

| | Claude Haiku 4.5 | GPT-5.3 Codex |
|---|---|---|
| Provider | Anthropic | OpenAI |
| Noometry Index | 39.3 | 45.5 |
| Rank | 155 | 61 |
| Context | 200K | 400K |
| Input $/M | $1 | $1.75 |
| Output $/M | $5 | $14 |
| Weights | Proprietary | Proprietary |

## Coding

- Claude Haiku 4.5: 43.9 (#71)
- GPT-5.3 Codex: 48.5 (#51)

| Benchmark | Claude Haiku 4.5 | GPT-5.3 Codex |
|---|---|---|
| LMArena WebDev | 1330 | 1408 |
| WeirdML | 45.4% | 79.3% |
| ALE-Bench | 653.48 | 1,655 |
| SWE-bench Verified | — | 74.8% |
| SWE-bench Verified (bash only) | 66.6% | — |
| SWE-bench Multilingual | 64.7% | — |
| SciCode | 43.3% | — |
| LMArena Coding | 1454 | — |

## Agentic & Tool Use

- Claude Haiku 4.5: 33.5 (#50)
- GPT-5.3 Codex: 47.8 (#9)

| Benchmark | Claude Haiku 4.5 | GPT-5.3 Codex |
|---|---|---|
| Terminal-Bench | 35.5% | 78.4% |
| Vending-Bench 2 | 458.89 | 5,940 |
| Berkeley Function Calling Leaderboard | 68.7% | — |
| DeepResearch Bench | 45.5% | — |
| BALROG | 31.2% | — |
| ExploitBench | 13.7% | — |
| METR Time Horizons | — | 74.5% |

## Reasoning

- Claude Haiku 4.5: 15.1 (#310)
- GPT-5.3 Codex: —

| Benchmark | Claude Haiku 4.5 | GPT-5.3 Codex |
|---|---|---|
| Epoch Capabilities Index | 142.42 | 156.84 |
| ARC-AGI-2 | 4% | — |
| NYT Connections (extended) | 14.3% | — |
| ARC-AGI-1 | 47.7% | — |
| CritPt | 0% | — |
| Chess Puzzles | 8% | — |
| LMArena Hard Prompts | 1420 | — |
| DTBench | 73.6% | — |
| LMCA | 30.9% | — |
| ForecastBench | 61.4 | — |

## Math

- Claude Haiku 4.5: 43.0 (#69)
- GPT-5.3 Codex: —

| Benchmark | Claude Haiku 4.5 | GPT-5.3 Codex |
|---|---|---|
| OTIS Mock AIME 2024-2025 | 66.7% | — |
| Omni-MATH | 56.1% | — |
| LMArena Math | 1395 | — |
| MATH Level 5 | 96.4% | — |
| FrontierMath (Feb 2025 set) | 5.9% | — |
| FrontierMath Tier 4 (v1) | 2.1% | — |

## Knowledge

- Claude Haiku 4.5: 37.6 (#146)
- GPT-5.3 Codex: —

| Benchmark | Claude Haiku 4.5 | GPT-5.3 Codex |
|---|---|---|
| GPQA Diamond | 71.2% | — |
| SimpleQA Verified | 13.2% | — |
| MMLU-Pro | 77.7% | — |
| Vectara Hallucination Rate | 9.8% | — |
| GPQA (HELM) | 60.5% | — |
| LMArena Expert | 1443 | — |

## Multimodal

- Claude Haiku 4.5: 27.0 (#111)
- GPT-5.3 Codex: —

| Benchmark | Claude Haiku 4.5 | GPT-5.3 Codex |
|---|---|---|
| Blueprint-Bench 2 | 0% | — |
| LMArena Document | 1420 | — |

## Multilingual

- Claude Haiku 4.5: 50.1 (#124)
- GPT-5.3 Codex: —

| Benchmark | Claude Haiku 4.5 | GPT-5.3 Codex |
|---|---|---|
| LMArena Non-English | 1376 | — |
| LMArena Chinese | 1415 | — |
| LMArena French | 1408 | — |
| LMArena German | 1377 | — |
| LMArena Japanese | 1339 | — |
| LMArena Korean | 1346 | — |
| LMArena Russian | 1380 | — |
| LMArena Spanish | 1420 | — |

## Instruction Following

- Claude Haiku 4.5: 71.6 (#144)
- GPT-5.3 Codex: —

| Benchmark | Claude Haiku 4.5 | GPT-5.3 Codex |
|---|---|---|
| IFEval | 80.1% | — |
| LMArena Instruction Following | 1414 | — |

## Long Context

- Claude Haiku 4.5: 43.6 (#86)
- GPT-5.3 Codex: —

| Benchmark | Claude Haiku 4.5 | GPT-5.3 Codex |
|---|---|---|
| LMArena Longer Query | 1427 | — |

## Writing & Preference

- Claude Haiku 4.5: 58.1 (#119)
- GPT-5.3 Codex: —

| Benchmark | Claude Haiku 4.5 | GPT-5.3 Codex |
|---|---|---|
| LMArena Text | 1396 | — |
| LMArena Creative Writing | 1371 | — |
| WildBench | 83.9% | — |
| EQ-Bench 4 | 1064 | — |
| LMArena Multi-Turn | 1409 | — |

## FAQ

### Is Claude Haiku 4.5 better than GPT-5.3 Codex?

GPT-5.3 Codex is the stronger model overall, scoring 45.5 to 39.3 on the Noometry Index. Claude Haiku 4.5 costs 2.4× less per token, which makes it the better buy when GPT-5.3 Codex's lead doesn't matter for your workload.

### Which is cheaper, Claude Haiku 4.5 or GPT-5.3 Codex?

Claude Haiku 4.5 is cheaper. It lists at $1 per million input tokens and $5 per million output tokens; GPT-5.3 Codex lists at $1.75 and $14.

### Is Claude Haiku 4.5 or GPT-5.3 Codex better for coding?

GPT-5.3 Codex scores higher on coding benchmarks: 48.5 versus 43.9 in the Noometry coding category.

### Which has the bigger context window?

GPT-5.3 Codex does, with 400K tokens against 200K.

### How many benchmarks do Claude Haiku 4.5 and GPT-5.3 Codex share?

6 benchmarks have published results for both models. Claude Haiku 4.5 has 53 scored results on Noometry and GPT-5.3 Codex has 8.