Claude Haiku 4.5 vs GPT-5.3-Codex
GPT-5.3-Codex leads Claude Haiku 4.5 on coding, 60.0 to 35.6 on the Vibe Coding Index, and Claude Haiku 4.5 costs 2.8x less per output token ($5.00 vs $14.00 per million). Updated October 22, 2026; 19 benchmarks have a result for both.
Claude Haiku 4.5 or GPT-5.3-Codex: which to pick
Pick GPT-5.3-Codex for coding: it scores 60.0 on the Vibe Coding Index against 35.6 for Claude Haiku 4.5, though on 3 benchmarks against 4.
Pick Claude Haiku 4.5 for output tokens 2.8x cheaper ($5.00 vs $14.00 per million) and a lower cost per solved task ($0.56 against $0.59).
- Shared results
- 19
- Claude Haiku 4.5 only
- 76
- GPT-5.3-Codex only
- 15
- Like-for-like categories
- 4 of 9
Only benchmarks that tested both models count toward a pick; a result only one model has is shown but not compared.
Which one for your work
Each card is decided by one number both models carry. Where only one has it, the card says so and makes no call.
Coding
Vibe Coding Index, six public coding benchmarks
GPT-5.3-Codex
GPT-5.3-Codex scores 60.0 against 35.6.
4 and 3 benchmarks behind the scores
Intelligence
AA Intelligence Index
GPT-5.3-Codex
GPT-5.3-Codex leads, 32.5 against 16.9.
Same board, both measured
Agentic work
AA Agentic Index
No call
Only Claude Haiku 4.5 has a AA Agentic Index result, 8.0.
Directional only
Reasoning
GPQA Diamond
No call
Only Claude Haiku 4.5 has a GPQA Diamond result, 72.2%.
Directional only
Math
AIME
No call
Only Claude Haiku 4.5 has a AIME result, 82.7%.
Directional only
Knowledge
MMLU Pro
No call
Only Claude Haiku 4.5 has a MMLU Pro result, 78.7%.
Directional only
Web design
Design Arena: websites
GPT-5.3-Codex
GPT-5.3-Codex leads, 1162 against 1122.
Same board, both measured
Long documents
Listed context window
GPT-5.3-Codex
GPT-5.3-Codex takes 400K tokens against 200K.
Listed by OpenRouter
Everyday cost
1K input + 500 output tokens
Claude Haiku 4.5
Claude Haiku 4.5 $0.0035, GPT-5.3-Codex $0.0088 per turn.
List prices
Cost per solved task
One SWE-bench Verified task, solved
Claude Haiku 4.5
Claude Haiku 4.5 $0.56, GPT-5.3-Codex $0.59.
Published attempt cost and pass rate
At a glance
- Coding index
- Claude Haiku 4.5 35.6 from 4 benchmarks, GPT-5.3-Codex 60.0 from 3 benchmarks.
- Widest category
- Agentic work, 34.3 points apart on average over 1 shared benchmark.
- Widest single gap
- Vibe Code Bench (Vals AI): Claude Haiku 4.5 11.4% against GPT-5.3-Codex 61.8%, 50.4 points apart on a 0 to 100 scale.
- Output price
- Claude Haiku 4.5 $5.00 against GPT-5.3-Codex $14.00 per million output tokens, 2.8x apart.
- Context window
- Claude Haiku 4.5 takes 200K tokens, GPT-5.3-Codex 400K.
Benchmark by benchmark
First the score: boards each model leads, per category, where both have a result. Then every board, a category to a fold; bars are each result on a 0 to 100 scale, so compare the two in a row, not one row with another.
| Category | Claude Haiku 4.5 | Bars | GPT-5.3-Codex | Ties |
|---|---|---|---|---|
| Coding | 0 | 8 | ||
| Intelligence | 0 | 2 | ||
| Agentic work | 0 | 1 | ||
| Web design | 1 | 7 | ||
| All shared boards | 1 | 18 |
CodingLike-for-likeSix of these boards feed the Vibe Coding Index08
| Benchmark | Claude Haiku 4.5 | GPT-5.3-Codex | Leader |
|---|---|---|---|
| SWE-bench Verified | 66.6% | Not measured | Only Claude Haiku 4.5 measured |
| SWE-bench Multilingual | 64.7% | Not measured | Only Claude Haiku 4.5 measured |
| LiveBench coding | 48.1% | Not measured | Only Claude Haiku 4.5 measured |
| LMArena WebDev | 1330 | 1407 | GPT-5.3-Codex |
| AA Coding Index | 43.9 | Not measured | Only Claude Haiku 4.5 measured |
| LiveCodeBench (Vals AI) | 41.2% | 87.3% | GPT-5.3-Codex |
| SWE-bench Verified (Vals AI) | 66.6% | 78.0% | GPT-5.3-Codex |
| Vibe Code Bench (Vals AI) | 11.4% | 61.8% | GPT-5.3-Codex |
| Code migration (Vals AI) | 7.5% | Not measured | Only Claude Haiku 4.5 measured |
| ProgramBench (Vals AI) | 0.0% | Not measured | Only Claude Haiku 4.5 measured |
| IOI (Vals AI) | Not measured | 53.8% | Only GPT-5.3-Codex measured |
| Terminal-Bench 2.1 (Vals AI) | 43.8% | Not measured | Only Claude Haiku 4.5 measured |
| Terminal-Bench Hard (Artificial Analysis run) | Not measured | 53.0% | Only GPT-5.3-Codex measured |
| Terminal-Bench | 35.5% | 78.4% | GPT-5.3-Codex |
| WeirdML | 45.4% | 79.3% | GPT-5.3-Codex |
| ALE-Bench | Not measured | 1655.2 | Only GPT-5.3-Codex measured |
| LMArena Text: coding | 1454 | Not measured | Only Claude Haiku 4.5 measured |
| LMArena Text: software and IT | 1439 | Not measured | Only Claude Haiku 4.5 measured |
| LMArena WebDev: React | 1326 | 1395 | GPT-5.3-Codex |
| LMArena WebDev: HTML | 1318 | 1422 | GPT-5.3-Codex |
| LMArena WebDev: from an image | Not measured | 1443 | Only GPT-5.3-Codex measured |
| Neither model has a result on the other 18 boards here | |||
IntelligenceLike-for-likePublic boards, shown as published, kept out of the index02
| Benchmark | Claude Haiku 4.5 | GPT-5.3-Codex | Leader |
|---|---|---|---|
| AA Intelligence Index | 16.9 | 32.5 | GPT-5.3-Codex |
| Epoch Capabilities Index | 142.2 | 157.1 | GPT-5.3-Codex |
Agentic workLike-for-likePublic boards, shown as published, kept out of the index01
| Benchmark | Claude Haiku 4.5 | GPT-5.3-Codex | Leader |
|---|---|---|---|
| AA Agentic Index | 8.0 | Not measured | Only Claude Haiku 4.5 measured |
| Design Arena: agents building web apps | Not measured | 1032 | Only GPT-5.3-Codex measured |
| Design Arena: agents building full-stack apps | Not measured | 983 | Only GPT-5.3-Codex measured |
| Design Arena: agents building mobile apps | Not measured | 1053 | Only GPT-5.3-Codex measured |
| Design Arena: agents building Android apps | Not measured | 1084 | Only GPT-5.3-Codex measured |
| Design Arena: agents building Godot games | Not measured | 1124 | Only GPT-5.3-Codex measured |
| LiveBench research | 42.2% | Not measured | Only Claude Haiku 4.5 measured |
| LiveBench browser | 40.0% | Not measured | Only Claude Haiku 4.5 measured |
| LiveBench long agentic | 11.1% | Not measured | Only Claude Haiku 4.5 measured |
| Finance Agent (Vals AI) | 31.0% | Not measured | Only Claude Haiku 4.5 measured |
| Tax Agent Bench (Vals AI) | 5.9% | Not measured | Only Claude Haiku 4.5 measured |
| Legal Research Bench (Vals AI) | 10.6% | Not measured | Only Claude Haiku 4.5 measured |
| Excel Modeling Benchmark (Vals AI) | 22.7% | Not measured | Only Claude Haiku 4.5 measured |
| τ²-Bench Telecom | Not measured | 86.0% | Only GPT-5.3-Codex measured |
| METR time horizons | Not measured | 74.5% | Only GPT-5.3-Codex measured |
| BALROG | 31.2% | Not measured | Only Claude Haiku 4.5 measured |
| DeepResearch Bench | 45.5% | Not measured | Only Claude Haiku 4.5 measured |
| Vending-Bench 2 | $459 | $5,940 | GPT-5.3-Codex |
| Berkeley Function Calling Leaderboard (BFCL) | 68.7% | Not measured | Only Claude Haiku 4.5 measured |
| Neither model has a result on the other 22 boards here | |||
ReasoningDirectional onlyPublic boards, shown as published, kept out of the index12 boards, none shared
| Benchmark | Claude Haiku 4.5 | GPT-5.3-Codex | Leader |
|---|---|---|---|
| GPQA Diamond | 72.2% | Not measured | Only Claude Haiku 4.5 measured |
| LiveBench reasoning | 61.7% | Not measured | Only Claude Haiku 4.5 measured |
| MMMU Pro (Vals AI) | 46.1% | Not measured | Only Claude Haiku 4.5 measured |
| GPQA Diamond (Artificial Analysis run) | Not measured | 91.5% | Only GPT-5.3-Codex measured |
| Humanity's Last Exam (Artificial Analysis run) | Not measured | 42.5% | Only GPT-5.3-Codex measured |
| GPQA Diamond (Epoch AI run) | 71.2% | Not measured | Only Claude Haiku 4.5 measured |
| ARC-AGI | 47.7% | Not measured | Only Claude Haiku 4.5 measured |
| ARC-AGI-2 | 4.0% | Not measured | Only Claude Haiku 4.5 measured |
| Chess puzzles | 8.0% | Not measured | Only Claude Haiku 4.5 measured |
| LMArena Text: hard prompts | 1421 | Not measured | Only Claude Haiku 4.5 measured |
| LMArena Text: expert | 1443 | Not measured | Only Claude Haiku 4.5 measured |
| LMArena Text: hard prompts in English | 1435 | Not measured | Only Claude Haiku 4.5 measured |
| Neither model has a result on the other 10 boards here | |||
MathDirectional onlyPublic boards, shown as published, kept out of the index6 boards, none shared
| Benchmark | Claude Haiku 4.5 | GPT-5.3-Codex | Leader |
|---|---|---|---|
| AIME | 82.7% | Not measured | Only Claude Haiku 4.5 measured |
| MGSM (Vals AI) | 92.2% | Not measured | Only Claude Haiku 4.5 measured |
| OTIS Mock AIME | 66.7% | Not measured | Only Claude Haiku 4.5 measured |
| MATH level 5 | 96.4% | Not measured | Only Claude Haiku 4.5 measured |
| LMArena Text: math | 1398 | Not measured | Only Claude Haiku 4.5 measured |
| LMArena Text: mathematical work | 1430 | Not measured | Only Claude Haiku 4.5 measured |
| Neither model has a result on the other 7 boards here | |||
KnowledgeDirectional onlyPublic boards, shown as published, kept out of the index13 boards, none shared
| Benchmark | Claude Haiku 4.5 | GPT-5.3-Codex | Leader |
|---|---|---|---|
| MMLU Pro | 78.7% | Not measured | Only Claude Haiku 4.5 measured |
| LegalBench (Vals AI) | 81.2% | Not measured | Only Claude Haiku 4.5 measured |
| CaseLaw (Vals AI) | 56.5% | Not measured | Only Claude Haiku 4.5 measured |
| CorpFin (Vals AI) | 60.6% | Not measured | Only Claude Haiku 4.5 measured |
| TaxEval (Vals AI) | 67.5% | Not measured | Only Claude Haiku 4.5 measured |
| MortgageTax (Vals AI) | 62.2% | Not measured | Only Claude Haiku 4.5 measured |
| MedQA (Vals AI) | 79.6% | Not measured | Only Claude Haiku 4.5 measured |
| MedCode (Vals AI) | 32.7% | Not measured | Only Claude Haiku 4.5 measured |
| SimpleQA Verified | 13.2% | Not measured | Only Claude Haiku 4.5 measured |
| LMArena Text: science | 1406 | Not measured | Only Claude Haiku 4.5 measured |
| LMArena Text: medicine and healthcare | 1394 | Not measured | Only Claude Haiku 4.5 measured |
| LMArena Text: legal and government | 1400 | Not measured | Only Claude Haiku 4.5 measured |
| LMArena Text: business and finance | 1400 | Not measured | Only Claude Haiku 4.5 measured |
| Neither model has a result on the other 5 boards here | |||
Web designLike-for-likePublic boards, shown as published, kept out of the index17
| Benchmark | Claude Haiku 4.5 | GPT-5.3-Codex | Leader |
|---|---|---|---|
| Design Arena: websites | 1122 | 1162 | GPT-5.3-Codex |
| Design Arena: UI components | 1097 | 1134 | GPT-5.3-Codex |
| Design Arena: game dev | 1097 | 1161 | GPT-5.3-Codex |
| Design Arena: data viz | 1124 | 1164 | GPT-5.3-Codex |
| Design Arena: 3D | 1071 | 1007 | Claude Haiku 4.5 |
| Design Arena: all code categories | 1114 | 1144 | GPT-5.3-Codex |
| Design Arena: SVG | 1025 | 1129 | GPT-5.3-Codex |
| Design Arena: ASCII art | 1140 | 1152 | GPT-5.3-Codex |
| LiveBench design | 59.9% | Not measured | Only Claude Haiku 4.5 measured |
GeneralDirectional onlyPublic boards, shown as published, kept out of the index28 boards, none shared
| Benchmark | Claude Haiku 4.5 | GPT-5.3-Codex | Leader |
|---|---|---|---|
| LiveBench data analysis | 59.3% | Not measured | Only Claude Haiku 4.5 measured |
| Public Benefits Bench (Vals AI) | 54.3% | Not measured | Only Claude Haiku 4.5 measured |
| SAGE (Vals AI) | 31.8% | Not measured | Only Claude Haiku 4.5 measured |
| MedScribe (Vals AI) | 85.2% | Not measured | Only Claude Haiku 4.5 measured |
| Vals Multimodal Index | 42.9% | Not measured | Only Claude Haiku 4.5 measured |
| IFBench | Not measured | 75.4% | Only GPT-5.3-Codex measured |
| Long context reasoning (AA-LCR) | Not measured | 83.3% | Only GPT-5.3-Codex measured |
| DTBench | 73.6% | Not measured | Only Claude Haiku 4.5 measured |
| LMCA | 30.9% | Not measured | Only Claude Haiku 4.5 measured |
| ExploitBench | 13.7% | Not measured | Only Claude Haiku 4.5 measured |
| LMArena Text | 1397 | Not measured | Only Claude Haiku 4.5 measured |
| LMArena Text: instruction following | 1414 | Not measured | Only Claude Haiku 4.5 measured |
| LMArena Text: creative writing | 1371 | Not measured | Only Claude Haiku 4.5 measured |
| LMArena Text: English | 1414 | Not measured | Only Claude Haiku 4.5 measured |
| LMArena Text: non-English | 1377 | Not measured | Only Claude Haiku 4.5 measured |
| LMArena Text: Chinese | 1418 | Not measured | Only Claude Haiku 4.5 measured |
| LMArena Text: German | 1374 | Not measured | Only Claude Haiku 4.5 measured |
| LMArena Text: Japanese | 1338 | Not measured | Only Claude Haiku 4.5 measured |
| LMArena Text: Korean | 1346 | Not measured | Only Claude Haiku 4.5 measured |
| LMArena Text: French | 1411 | Not measured | Only Claude Haiku 4.5 measured |
| LMArena Text: Spanish | 1421 | Not measured | Only Claude Haiku 4.5 measured |
| LMArena Text: Russian | 1381 | Not measured | Only Claude Haiku 4.5 measured |
| LMArena Text: Polish | 1343 | Not measured | Only Claude Haiku 4.5 measured |
| LMArena Text: multi-turn | 1409 | Not measured | Only Claude Haiku 4.5 measured |
| LMArena Text: longer queries | 1427 | Not measured | Only Claude Haiku 4.5 measured |
| LMArena Text: writing and language | 1386 | Not measured | Only Claude Haiku 4.5 measured |
| LMArena Text: entertainment and media | 1369 | Not measured | Only Claude Haiku 4.5 measured |
| LMArena Document | 1420 | Not measured | Only Claude Haiku 4.5 measured |
| Neither model has a result on the other 18 boards here | |||
Image generationNot comparablePublic boards, shown as published, kept out of the index0 boards, none shared
| Benchmark | Claude Haiku 4.5 | GPT-5.3-Codex | Leader |
|---|---|---|---|
| Neither model has a result on 5 boards here | |||
What each workload costs
Three fixed token mixes at each model's listed price, so per-token rates become a bill.
Chat turn
1K input + 500 output tokens
Claude Haiku 4.5 costs less
Repository review
50K input + 3K output tokens
Claude Haiku 4.5 costs less
Agent loop
200K input + 10K output tokens
Claude Haiku 4.5 costs less
Listed OpenRouter rates, without cache or batch discounts. A workload that does not fit a model's context window would need splitting.
Specification differences
The leading value in a row is marked; a row with a missing value names no leader. Our two rows are marked in red.
| Specification | Anthropic Claude Haiku 4.5 | OpenAI GPT-5.3-Codex |
|---|---|---|
| Maker | Anthropic | OpenAI |
| Weights | No public weights listed | No public weights listed |
| Released | October 15, 2025 | February 24, 2026 |
| Context window | 200K | 400K |
| Input $/Mtok | $1.00 | $1.75 |
| Output $/Mtok | $5.00 | $14.00 |
| Coding index | 35.6 | 60.0 |
| Independent measurements behind it | 4 | 3 |
| Cost per solved task | $0.56 | $0.59 |
| Source | OpenRouter listing | OpenRouter listing |
Questions
Is Claude Haiku 4.5 better than GPT-5.3-Codex for coding?
Pick GPT-5.3-Codex for coding: it scores 60.0 on the Vibe Coding Index against 35.6 for Claude Haiku 4.5, though on 3 benchmarks against 4. Pick Claude Haiku 4.5 for output tokens 2.8x cheaper ($5.00 vs $14.00 per million) and a lower cost per solved task ($0.56 against $0.59).
Which is cheaper, Claude Haiku 4.5 or GPT-5.3-Codex?
Claude Haiku 4.5 costs $1.00 per million input tokens and $5.00 per million output tokens; GPT-5.3-Codex costs $1.75 and $14.00. Per solved SWE-bench Verified task, Claude Haiku 4.5 costs $0.56 and GPT-5.3-Codex $0.59.
Which has the bigger context window, Claude Haiku 4.5 or GPT-5.3-Codex?
GPT-5.3-Codex, with 400K tokens against 200K.
How many benchmarks is the Claude Haiku 4.5 vs GPT-5.3-Codex comparison based on?
19 benchmarks have a result for both models as of October 22, 2026. Claude Haiku 4.5's coding index rests on 4 independent benchmarks and GPT-5.3-Codex's on 3. Where only one model has a result, the page says so instead of naming a winner.