Claude Opus 4.7 vs GPT-5.3-Codex
Full profiles: Claude Opus 4.7 (Anthropic) and GPT-5.3-Codex (OpenAI): every benchmark, price and rank.
Updated · Data from 9 sources
As of 26 Oct 2026, Claude Opus 4.7 and GPT-5.3-Codex (xhigh) are neck and neck on quality (63.2 vs 63.8 on the BenchLeader Index, inside the ±5.5 margin of error), and GPT-5.3-Codex (xhigh) costs 2.1× less, which makes it the better buy for most workloads. Pick Claude Opus 4.7 for multimodal (67 vs 62); pick GPT-5.3-Codex for agents & tools (80 vs 65).
Claude Opus 4.7 benchmarks Anthropic
GPT-5.3-Codex benchmarks xhigh OpenAIOpen in the comparison toolWhich to pick for…
The pick's numbers come first. Category scores on the BenchLeader scale (50 is the average model, 15 points one standard deviation). High confidence means a lead of half a standard deviation or more; under one point is called either.
| Use case | Pick | Why | Confidence |
|---|---|---|---|
| Coding | Either | 61 vs 61 category score; LiveCodeBench 85.1% vs 87.3% | Either |
| Agents & tool use | GPT-5.3-Codex | 80 vs 65 category score | High |
| Reasoning | GPT-5.3-Codex | 69 vs 65 category score | Medium |
| Knowledge | GPT-5.3-Codex | 66 vs 62 category score | Medium |
| Images and other media | Claude Opus 4.7 | 67 vs 62 category score | Medium |
| Chat quality (human preference) | – | Only Claude Opus 4.7 has an Arena rating (68 category score) | Not comparable |
| Lowest cost | GPT-5.3-Codex | $4.81 vs $10.00 per 1M blended; agentic coding session $0.161 vs $0.400 | – |
| Fastest first answer | Claude Opus 4.7 | 1.2 s (Claude Opus 4.7) vs 55.6 s (GPT-5.3-Codex (xhigh)) to the first answer token | – |
| High volume on a budget | Neither flagship | A smaller model is usually the better fit: Cheapest good LLMs, Fastest LLMs | – |
Key differences
- Claude Opus 4.7 and GPT-5.3-Codex (xhigh) are neck and neck on quality (63.2 vs 63.8, within the ±5.5 margin).
- Claude Opus 4.7 is stronger in coding, human preference, maths, multimodal.
- GPT-5.3-Codex (xhigh) is stronger in agents & tools, knowledge, reasoning, composite, instruction following, long context.
- GPT-5.3-Codex (xhigh) is 2.1× cheaper ($4.81 vs $10.00 per 1M blended).
- GPT-5.3-Codex (xhigh) streams 1.8× faster (105 vs 60 tokens per second).
At a glance
| Spec | Claude Opus 4.7 | GPT-5.3-Codex |
|---|---|---|
| Developer | Anthropic | OpenAI |
| Released | 16 Apr 2026 | 5 Feb 2026 |
| Context window (tokens) | 1,000,000 | 400,000 |
| Compared at | Claude Opus 4.7 | GPT-5.3-Codex (xhigh) |
| Open weights | No | No |
| Price in / out / cached input per 1M | $5.00 / $25.00 / $0.500 | $1.75 / $14.00 / $0.175 |
Category by category
Category scores on the BenchLeader scale: 50 is the average evaluated model, 15 points is one standard deviation. A gap under one point is called level.
| Category | Claude Opus 4.7 | GPT-5.3-Codex (xhigh) | Leader |
|---|---|---|---|
| Reasoning | 64.8 | 68.6 | GPT-5.3-Codex |
| Coding | 61.4 | 60.9 | level |
| Agents & tools | 65.4 | 80.1 | GPT-5.3-Codex |
| Maths | 66.7 | – | not comparable |
| Knowledge | 62.3 | 66.4 | GPT-5.3-Codex |
| Instruction following | – | 73.5 | not comparable |
| Human preference | 67.5 | – | not comparable |
| Multimodal | 66.8 | 61.5 | Claude Opus 4.7 |
| Long context | – | 66.8 | not comparable |
| Composite | – | 65.8 | not comparable |
Where sources disagree
Across the 5 benchmarks both have, Claude Opus 4.7 leads 2, GPT-5.3-Codex leads 3. Every overall index that rates both puts GPT-5.3-Codex ahead.
| Overall index | Claude Opus 4.7 | GPT-5.3-Codex (xhigh) | Leader |
|---|---|---|---|
| BenchLeader Index | 63.2 | 63.8 | neck and neck |
| AA Intelligence Index v4.3.2 | 30.9† | 32.5 | GPT-5.3-Codex |
Each publisher runs its own tasks, harnesses and effort settings, so their overall verdicts can differ; BenchLeader reconciles them on one scale. † measured at another effort setting of the same model (the publisher did not test the one compared here).
Price, speed and latency
List prices blended at three input tokens per output token. Per-task prices use the token counts on the methodology page; suite cost is what Artificial Analysis paid to run its whole Intelligence Index.
| Measure | Claude Opus 4.7 | GPT-5.3-Codex (xhigh) |
|---|---|---|
| Blended price per 1M tokens | $10.00 | $4.81 |
| Input / output per 1M | $5.00 / $25.00 | $1.75 / $14.00 |
| Cached input per 1M | $0.500 | $0.175 |
| Price per task: chat reply | $0.0095 | $0.0049 |
| Price per task: summarize a 30-page report | $0.075 | $0.029 |
| Price per task: code edit | $0.068 | $0.032 |
| Price per task: agentic coding session | $0.400 | $0.161 |
| Price per task: structured extraction | $0.015 | $0.0063 |
| Output speed | 60 tok/s | 105 tok/s |
| Time to first answer | 1.2 s | 55.6 s |
| Time to a complete answer | – | 60.4 s |
Each model is compared at its best-ranked reasoning-effort setting. Claude Opus 4.7 scores 63.2 and takes 1 s to its first answer token; Claude Opus 4.7 (max) scores 62.4 in 25 s, Claude Opus 4.7 (high) scores 61.0 in 1 s. GPT-5.3-Codex (xhigh) scores 63.8 and takes 56 s to its first answer token. Higher effort means more thinking before the answer: better scores, longer waits and more tokens billed. How reasoning effort changes scores and cost.
Every benchmark
107 benchmarks, grouped by category; each name links to the board and its leaders. † measured at another effort setting of the same model, shown for reference and not counted as a win.
| Metric | Publisher | Claude Opus 4.7 | GPT-5.3-Codex (xhigh) |
|---|---|---|---|
| BenchLeader Index | BenchLeader | 63.2 | 63.8 |
| Agents & tools score | BenchLeader | 65.4 | 80.1 |
| Coding score | BenchLeader | 61.4 | 60.9 |
| Human preference score | BenchLeader | 67.5 | – |
| Knowledge score | BenchLeader | 62.3 | 66.4 |
| Maths score | BenchLeader | 66.7 | – |
| Multimodal score | BenchLeader | 66.8 | 61.5 |
| Reasoning score | BenchLeader | 64.8 | 68.6 |
| Composite score | BenchLeader | – | 65.8 |
| Instruction following score | BenchLeader | – | 73.5 |
| Long context score | BenchLeader | – | 66.8 |
| Blended price $/M | BenchLeader | $10.00 | $4.81 |
| Output speed | BenchLeader | 60 tok/s | 105 tok/s |
| Time to first answer | BenchLeader | 1.2 s | 55.6 s |
| Context window | BenchLeader | 1,000,000 tokens | 400,000 tokens |
| Reasoning | |||
| GPQA Diamond | Epoch AI Benchmarking Hub | 86.4%† | – |
| Humanity's Last Exam | Scale AI / CAIS | 36.2% | – |
| SimpleBench | SimpleBench | 61.7% | – |
| Arena Hard Prompts | Arena (LMArena) | 1519 | – |
| LiveBench 1.0 Reasoning | LiveBench | 87.2%† | – |
| GPQA Diamond (AA) | Artificial Analysis | 91.4%† | 91.5% |
| Humanity's Last Exam (AA) | Artificial Analysis | 42.3%† | 42.5% |
| GPQA Diamond (Vals) | Vals AI | 90.2% | – |
| Kagi LLM Benchmark | Kagi LLM Benchmark | 73.3% | – |
| ARC-AGI-3 | ARC Prize | 0.2%† | – |
| CritPt | Artificial Analysis | 12.0%† | 16.9% |
| Chess Puzzles | Epoch AI Benchmarking Hub | 41.0%† | – |
| Mystery Game Puzzles | Epoch AI Benchmarking Hub | 13.0% | – |
| LMCA | Epoch AI Benchmarking Hub | 52.2%† | – |
| DTBench | Epoch AI Benchmarking Hub | 94.7%† | – |
| ForecastBench | ForecastBench | 63.1% | – |
| Coding | |||
| SWE-bench Verified (Epoch) | Epoch AI Benchmarking Hub | 83.5%† | 74.8%† |
| SciCode | SciCode | 54.5%† | – |
| WeirdML | WeirdML | 76.4% | 77.9% |
| FrontierCode | Cognition | 38.5% | – |
| GSO-Bench | GSO-Bench | 44.1% | – |
| Arena Coding | Arena (LMArena) | 1546 | – |
| Arena WebDev | Arena (LMArena) | 1555 | 1407† |
| LiveBench 1.0 Coding | LiveBench | 82.1%† | – |
| LiveCodeBench | Vals AI | 85.1% | 87.3% |
Show all 107 benchmarks
| Metric | Publisher | Claude Opus 4.7 | GPT-5.3-Codex (xhigh) |
|---|---|---|---|
| Coding | |||
| IOI | Vals AI | – | 53.8% |
| SWE-bench (Vals) | Vals AI | 82.0% | 78.0% |
| Code Migration | Vals AI | 43.9% | – |
| ProgramBench | Vals AI | 0.0% | – |
| Vibe Code Bench v1.1 | Vals AI | 71.0% | 61.8% |
| SWE Atlas: Codebase QnA | Scale AI SEAL | 40.3% | – |
| SWE Atlas: Refactoring | Scale AI SEAL | 48.6% | – |
| SWE Atlas: Test Writing | Scale AI SEAL | 38.5% | – |
| MirrorCode | Epoch AI Benchmarking Hub | 31.1%† | – |
| ALE-Bench | Epoch AI Benchmarking Hub | 1323.0 | 1655.2 |
| Terminal-Bench 2.1 (AA) | Artificial Analysis | 83.2%† | – |
| Agents & tools | |||
| Terminal-Bench | Terminal-Bench | 80.2% | 78.4%† |
| OSWorld-Verified 2.0 | OSWorld | 18.2%† | – |
| APEX-Agents | Mercor | 49.2%† | – |
| LiveBench Agentic Coding | LiveBench | 50.7%† | – |
| Terminal-Bench Hard | Artificial Analysis | 51.5%† | 53.0% |
| τ²-Bench Telecom (AA) | Artificial Analysis | 88.6%† | 86.0% |
| Terminal-Bench 2.1 (Vals) | Vals AI | 68.5% | – |
| MCP Atlas | Scale AI SEAL | 79.1%† | – |
| HiL-Bench | Scale AI SEAL | 41.7% | 4.3%† |
| τ³-bench Banking | τ²-bench | 40.2%† | – |
| τ³-Banking (AA) | Artificial Analysis | 34.6%† | – |
| ITBench SRE (AA) | Artificial Analysis | 46.7%† | – |
| Analyst Agent (AA) | Artificial Analysis | 43.8%† | – |
| Finance Agent v2 | Vals AI | 51.5% | – |
| Harvey's Legal Agent Benchmark v1 | Vals AI | 6.7% | – |
| Legal Research Bench | Vals AI | 38.5% | – |
| Tax Agent Bench | Vals AI | 23.7% | – |
| Terminal-Bench 2.0 (Vals) | Vals AI | 68.5% | 64.0%† |
| Arena Search | Arena (LMArena) | 1210 | – |
| PostTrainBench | Epoch AI Benchmarking Hub | 28.6%† | – |
| ExploitBench | Epoch AI Benchmarking Hub | 26.5% | – |
| METR Time Horizons | METR | – | 74.5%† |
| GBAEval | Epoch AI Benchmarking Hub | 43.8% | – |
| Vending-Bench 2 | Andon Labs | 10936.8 | 5940.1† |
| Maths | |||
| FrontierMath Tiers 1–3 | Epoch AI Benchmarking Hub | 70.2%† | – |
| FrontierMath Tier 4 | Epoch AI Benchmarking Hub | 31.7%† | – |
| OTIS Mock AIME | Epoch AI Benchmarking Hub | 86.7%† | – |
| ProofBench | Vals AI | 54.0%† | – |
| LiveBench Mathematics | LiveBench | 92.8%† | – |
| AIME (Vals) | Vals AI | 96.3% | – |
| AIME 2026 | MathArena | 95.8%† | – |
| HMMT February 2026 | MathArena | 93.9%† | – |
| MathArena Apex | MathArena | 40.6%† | – |
| Arena Maths | Arena (LMArena) | 1491 | – |
| Knowledge | |||
| SimpleQA Verified | Epoch AI Benchmarking Hub | 51.7%† | – |
| LiveBench 1.0 Data Analysis | LiveBench | 78.3%† | – |
| AA-Omniscience | Artificial Analysis | 27.3† | 10.9 |
| MMLU-Pro | Vals AI | 89.9% | – |
| LegalBench | Vals AI | 85.3% | – |
| CorpFin | Vals AI | 66.1% | – |
| TaxEval | Vals AI | 75.3% | – |
| CaseLaw v2 | Vals AI | 68.4% | – |
| Excel Modeling Benchmark | Vals AI | 63.7% | – |
| MedCode | Vals AI | 54.9% | – |
| MedScribe | Vals AI | 83.0% | – |
| EBR-bench | Epoch AI Benchmarking Hub | 19.1%† | – |
| GDP.pdf | Epoch AI Benchmarking Hub | 21.0%† | – |
| AA-Omniscience: accuracy | Artificial Analysis | 48.9%† | 52.9% |
| AA-Omniscience: non-hallucination | Artificial Analysis | 57.7%† | 10.8% |
| Instruction following | |||
| LiveBench Language | LiveBench | 77.9%† | – |
| LiveBench Instruction Following | LiveBench | 66.7%† | – |
| IFBench | Artificial Analysis | 58.6%† | 75.4% |
| Arena Instruction Following | Arena (LMArena) | 1493 | – |
| Human preference | |||
| Arena Text | Arena (LMArena) | 1494 | – |
| EQ-Bench 4 | EQ-Bench | 1294 | – |
| Arena Creative Writing | Arena (LMArena) | 1482 | – |
| Arena Multi-turn | Arena (LMArena) | 1514 | – |
| Arena Longer Queries | Arena (LMArena) | 1508 | – |
| Multimodal | |||
| Arena Vision | Arena (LMArena) | 1317 | – |
| MMMU-Pro | Artificial Analysis | 78.8%† | 78.5% |
| MMMU-Pro (Vals) | Vals AI | 85.5% | – |
| MortgageTax | Vals AI | 70.3% | – |
| SAGE | Vals AI | 56.1% | – |
| Blueprint-Bench 2 | Epoch AI Benchmarking Hub | 24.5% | – |
| Long context | |||
| AA-LCR | Artificial Analysis | 78.7%† | 83.3% |
| Arena Document | Arena (LMArena) | 1494 | – |
| Composite | |||
| Epoch Capabilities Index | Epoch AI Benchmarking Hub | 156.8 | 157.1† |
| LiveBench 1.0 | LiveBench | 76.5%† | – |
| AA Intelligence Index v4.3.2 | Artificial Analysis | 40.7† | 32.5 |
| Vals RSI Index | Vals AI | 19.1%† | – |
| Vals Multimodal Index | Vals AI | 67.4% | – |
Data as of 2026-10-26. Best configuration of each model; every score links to its source on the model pages.
What changed
- Claude Opus 4.7: 1 score revision.
- Claude Opus 4.7: 1 new result (Harvey's Legal Agent Benchmark v1).
- Claude Opus 4.7: 1 score revision.
- Claude Opus 4.7: 1 score revision.
- Claude Opus 4.7: 1 score revision.
- Claude Opus 4.7: BenchLeader Index 64.0 → 63.5.
- Claude Opus 4.7: 1 new result (SWE Atlas: Codebase QnA).
- GPT-5.3-Codex (xhigh): BenchLeader Index 64.4 → 63.9.
- Claude Opus 4.7: BenchLeader Index 64.5 → 64.0.
- Claude Opus 4.7: 1 new result (SWE Atlas: Test Writing).
Related comparisons
- Claude Opus 4.7 vs Claude Opus 5.5
- Claude Opus 4.7 vs Gemini 4 Argon
- Claude Opus 4.7 vs Muse Wonder Full
- GPT-5.3-Codex vs Claude Opus 5.5
- GPT-5.3-Codex vs Gemini 4 Argon
- GPT-5.3-Codex vs Muse Wonder Full
- GPT-5.3-Codex vs Claude Fable 5.1
- Claude Opus 4.6 vs GPT-5.3-Codex
Rankings: Best LLM overall · Best LLM for agents · Best cheap LLMs
Claude Opus 4.7 vs GPT-5.3-Codex: questions
- Is Claude Opus 4.7 better than GPT-5.3-Codex?
- Claude Opus 4.7 and GPT-5.3-Codex (xhigh) are neck and neck on quality (63.2 vs 63.8, within the ±5.5 margin). The BenchLeader Index combines every independent quality benchmark; the two are within the margin of error as of 2026-10-26, so check the category scores for your use.
- Which is cheaper, Claude Opus 4.7 or GPT-5.3-Codex, and will that change?
- GPT-5.3-Codex is cheaper today: $4.81 against $10.00 per million tokens, blended at three input tokens per output token. No price change has been announced for either.
- Is Claude Opus 4.7 better than GPT-5.3-Codex for coding?
- They are level in coding (61 vs 61 on the category index). On LiveCodeBench, the top shared coding benchmark, it is 85.1% for Claude Opus 4.7 against 87.3% for GPT-5.3-Codex.
- Is Claude Opus 4.7 better than GPT-5.3-Codex for agents and tool use?
- GPT-5.3-Codex scores higher in agentic tasks (80 vs 65 on the category index, where 50 is average).
- Which is better for long documents?
- Claude Opus 4.7 accepts more context: 1,000,000 against 400,000 tokens for GPT-5.3-Codex.
- Which has lower latency?
- Claude Opus 4.7 answers first: 1.2 s against 55.6 s to the first answer token, as Artificial Analysis measures it. Reasoning effort drives most of this: a higher setting thinks longer before it answers.
- Which is faster, Claude Opus 4.7 or GPT-5.3-Codex?
- GPT-5.3-Codex streams faster: 105 against 60 output tokens per second.
- Do independent benchmarks agree on Claude Opus 4.7 vs GPT-5.3-Codex?
- Across the 5 benchmarks both have, Claude Opus 4.7 leads 2, GPT-5.3-Codex leads 3. Every overall index that rates both puts GPT-5.3-Codex ahead.
- When was Claude Opus 4.7 released?
- Claude Opus 4.7 was released on 16 Apr 2026.