Claude Fable 5.2 vs GPT-5-Codex
Full profiles: Claude Fable 5.2 (Anthropic) and GPT-5-Codex (OpenAI): every benchmark, price and rank.
Updated · Data from 6 sources
As of 9 Nov 2026, Claude Fable 5.2 is ahead of GPT-5-Codex on quality (69.4 vs 58.6 on the BenchLeader Index), but GPT-5-Codex is 4.7× cheaper at $3.44 vs $16.00 per 1M tokens blended. Claude Fable 5.2 leads in every category we measure both in. GPT-5-Codex streams ~1.5× faster.
Key facts (as of 9 Nov 2026)
- Claude Fable 5.2 scores 69.4 on the BenchLeader Index and GPT-5-Codex scores 58.6 (BenchLeader, 9 Nov 2026).
- Claude Fable 5.2 costs $16.00 per 1M tokens blended (list price, 9 Nov 2026).
- GPT-5-Codex costs $3.44 per 1M tokens blended (list price, 9 Nov 2026).
- GPT-5-Codex streams ~1.5× faster than Claude Fable 5.2 (Artificial Analysis, 7-day median, 9 Nov 2026).
- Of the 3 benchmarks both models have, Claude Fable 5.2 leads 3 (BenchLeader, 9 Nov 2026).
Claude Fable 5.2 benchmarks 
GPT-5-Codex benchmarks Which 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 | Claude Fable 5.2 | 80 vs 59 category score | High |
| Long documents | Claude Fable 5.2 | 66 vs 60 category score; AA-LCR v1.1 83.3% vs 71.7% | Medium |
| Lowest cost | GPT-5-Codex | $3.44 vs $16.00 per 1M blended; agentic coding session $0.115 vs $0.640 | – |
| Fastest first answer | Claude Fable 5.2 | 3.0 s (Claude Fable 5.2 (max)) vs 9.9 s (GPT-5-Codex (high)) 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 Fable 5.2 (max) leads on quality: 69.4 vs 58.6.
- Claude Fable 5.2 (max) is stronger in coding, knowledge, long context, reasoning.
- GPT-5-Codex (high) is stronger in agents & tools, instruction following, multimodal.
- GPT-5-Codex (high) is 4.7× cheaper ($3.44 vs $16.00 per 1M blended).
- GPT-5-Codex (high) streams 1.8× faster (128 vs 72 tokens per second).
At a glance
| Spec | Claude Fable 5.2 | GPT-5-Codex |
|---|---|---|
| Developer | Anthropic | OpenAI |
| Released | 9 Nov 2026 | 15 Sep 2025 |
| Context window (tokens) | 1,000,000 | 400,000 |
| Compared at | Claude Fable 5.2 (max) | GPT-5-Codex (high) |
| Open weights | No | No |
| Price in / out / cached input per 1M | $8.00 / $40.00 / $0.200 | $1.25 / $10.00 / $0.125 |
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 Fable 5.2 (max) | GPT-5-Codex (high) | Leader |
|---|---|---|---|
| Reasoning | 78.4 | – | not comparable |
| Coding | 80.2 | 58.6 | Claude Fable 5.2 |
| Agents & tools | – | 66.8 | not comparable |
| Knowledge | 81.7 | – | not comparable |
| Instruction following | – | 72.5 | not comparable |
| Multimodal | – | 56.1 | not comparable |
| Long context | 66.4 | 60.3 | Claude Fable 5.2 |
On one chart
Each domain on the index's own scale, where 50 is an average model. Click a domain to see the benchmarks behind it. Pick up to 5 models from the list.
Dashed: that model has no result on the axis it crosses; the line only joins its neighbours.
Not drawn, no result for these models: Maths, Human preference.
Where sources disagree
Across the 3 benchmarks both have, Claude Fable 5.2 leads 3, GPT-5-Codex leads 0. Every overall index that rates both puts Claude Fable 5.2 ahead.
| Overall index | Claude Fable 5.2 (max) | GPT-5-Codex (high) | Leader |
|---|---|---|---|
| BenchLeader Index | 69.4 | 58.6 | Claude Fable 5.2 |
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. Cost, tokens and time per benchmark task are measured: Artificial Analysis’ averages over its whole Intelligence Index run.
| Measure | Claude Fable 5.2 (max) | GPT-5-Codex (high) |
|---|---|---|
| Blended price per 1M tokens | $16.00 | $3.44 |
| Input / output per 1M | $8.00 / $40.00 | $1.25 / $10.00 |
| Cached input per 1M | $0.200 | $0.125 |
| Price per task: chat reply | $0.015 | $0.0035 |
| Price per task: summarize a 30-page report | $0.120 | $0.021 |
| Price per task: code edit | $0.108 | $0.022 |
| Price per task: agentic coding session | $0.640 | $0.115 |
| Price per task: structured extraction | $0.024 | $0.0045 |
| Agentic coding session, input mostly cached | $0.289 | $0.064 |
| Cost per benchmark task (AA Index) | $26.54 | – |
| Output tokens per task (AA Index) | 274k | – |
| Of which reasoning | 193k | – |
| Output speed | 72 tok/s | 128 tok/s |
| Time to first answer | 3.0 s | 9.9 s |
| Time to a complete answer | – | 19.3 s |
Each model is compared at its best-ranked reasoning-effort setting. Claude Fable 5.2 (max) scores 69.4 and takes 3 s to its first answer token; Claude Fable 5.2 (high) scores 68.9 in 3 s, Claude Fable 5.2 (xhigh) scores 68.7 in 3 s. GPT-5-Codex (high) scores 58.6 and takes 10 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
61 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 Fable 5.2 (max) | GPT-5-Codex (high) |
|---|---|---|---|
| BenchLeader Index | BenchLeader | 69.4 | 58.6 |
| Coding score | BenchLeader | 80.2 | 58.6 |
| Knowledge score | BenchLeader | 81.7 | – |
| Long context score | BenchLeader | 66.4 | 60.3 |
| Reasoning score | BenchLeader | 78.4 | – |
| Agents & tools score | BenchLeader | – | 66.8 |
| Instruction following score | BenchLeader | – | 72.5 |
| Multimodal score | BenchLeader | – | 56.1 |
| Blended price $/M | BenchLeader | $16.00 | $3.44 |
| Output speed | BenchLeader | 72 tok/s | 128 tok/s |
| Time to first answer | BenchLeader | 3.0 s | 9.9 s |
| Context window | BenchLeader | 1,000,000 tokens | 400,000 tokens |
| Reasoning | |||
| LiveBench 2.0 Reasoning | LiveBench | 88.8%† | – |
| GPQA Diamond (AA) | Artificial Analysis | – | 83.7% |
| Humanity's Last Exam (AA) | Artificial Analysis | 74.3% | 27.9% |
| HLE-Diamond (AA) | Artificial Analysis | 80.2% | – |
| Kagi LLM Benchmark | Kagi LLM Benchmark | – | 70.3%† |
| ARC-AGI-1 | ARC Prize | 97.5% | – |
| ARC-AGI-2 | ARC Prize | 92.5% | – |
| ARC-AGI-3 | ARC Prize | 94.9% | – |
| CritPt | Artificial Analysis | 35.4% | 5.1% |
| CritPt v2 hard subset (AA) | Artificial Analysis | 76.5% | – |
| MysteryMechanism | Vals AI | 51.8%† | – |
| Coding | |||
| WeirdML | WeirdML | – | 54.5% |
| LiveBench 2.0 Coding | LiveBench | 79.2%† | – |
| SciCode (AA) | Artificial Analysis | 67.7% | – |
| LiveCodeBench | Vals AI | – | 84.7% |
| Code Migration v1.1 | Vals AI | 83.3%† | – |
| ProgramBench | Vals AI | 19.0%† | – |
| SWE-bench Multimodal | Vals AI | 62.3%† | – |
| Vibe Code Bench v1.1 | Vals AI | 93.8%† | – |
| Terminal-Bench 4.0 (AA) | Artificial Analysis | 59.6% | – |
| FrontierCode 1.1 (AA) | Artificial Analysis | 56.2% | – |
| FrontierCode 2.0 (AA) | Artificial Analysis | 48.8% | – |
| LiveCodeBench (AA) | Artificial Analysis | – | 84.0% |
| Agents & tools | |||
| Terminal-Bench | Terminal-Bench | – | 44.3%† |
| LiveBench Long Agentic | LiveBench | 82.9%† | – |
Show all 61 benchmarks
| Metric | Publisher | Claude Fable 5.2 (max) | GPT-5-Codex (high) |
|---|---|---|---|
| Agents & tools | |||
| LiveBench Browser | LiveBench | 86.9%† | – |
| Terminal-Bench Hard | Artificial Analysis | – | 37.9% |
| τ²-Bench Telecom (AA) | Artificial Analysis | – | 86.8% |
| GDPval-AA v2.1 | Artificial Analysis | 1831 | – |
| Finance Agent v2.1 | Vals AI | 69.5%† | – |
| Harvey's Legal Agent Benchmark v1.1 | Vals AI | 5.0%† | – |
| Legal Research Bench | Vals AI | 46.1%† | – |
| Tax Agent Bench | Vals AI | 32.1%† | – |
| Terminal-Bench 4.0 (Vals) | Vals AI | 65.2%† | – |
| Terminal-Bench Science | Vals AI | 55.7%† | – |
| AutomationBench | Artificial Analysis | 76.4% | – |
| GDP.pdf (AA) | Artificial Analysis | 24.2% | – |
| Harvey LAB-AA v1.1 | Harvey | 7.2% | – |
| AA-Briefcase v1.1 | Artificial Analysis | 1785 | – |
| Design Arena Agentic | Design Arena | – | 1046† |
| Terminal-Bench-Science 0.1 (AA) | Artificial Analysis | 73.3% | – |
| Knowledge | |||
| LiveBench 2.0 Data Analysis | LiveBench | 81.5%† | – |
| LiveBench Research | LiveBench | 94.5%† | – |
| AA-Omniscience v1.1 | Artificial Analysis | 66.8 | – |
| BioMysteryBench | Vals AI | 83.7%† | – |
| Excel Modeling Benchmark v1.1 | Vals AI | 77.4%† | – |
| AA-Omniscience v1.1: accuracy | Artificial Analysis | 79.1% | – |
| AA-Omniscience v1.1: non-hallucination | Artificial Analysis | 40.7% | – |
| HealthBench Professional (AA) | OpenAI | 67.7% | – |
| Instruction following | |||
| IFBench | Artificial Analysis | – | 74.2% |
| Multimodal | |||
| LiveBench Design | LiveBench | 85.1%† | – |
| MMMU-Pro | Artificial Analysis | – | 73.8% |
| AA-3D | Artificial Analysis | 1271 | – |
| Long context | |||
| AA-LCR v1.1 | Artificial Analysis | 83.3% | 71.7% |
| Composite | |||
| LiveBench 2.0 | LiveBench | 84.9%† | – |
| AA Intelligence Index v5.0 | Artificial Analysis | 64.6 | – |
| AA Intelligence Index v4.3.2 | Artificial Analysis | – | 24.9 |
| AA Progress Index v1 | Artificial Analysis | – | 275.1 |
| Vals Index | Vals AI | 71.3† | – |
| Vals Finance Index | Vals AI | 63.6%† | – |
| Vals Science Index | Vals AI | 63.7%† | – |
Data as of 2026-11-09. Best configuration of each model; every score links to its source on the model pages.
What changed
- GPT-5-Codex (high): 1 new result (AA Progress Index v1).
- GPT-5-Codex (high): BenchLeader Index 59.0 → 58.5.
Related comparisons
- Claude Fable 5.2 vs Claude Opus 5.5
- Claude Fable 5.2 vs Gemini 4 Carbon
- Claude Fable 5.2 vs GPT-6 Astra
- GPT-5-Codex vs Claude Opus 5.5
- GPT-5-Codex vs Gemini 4 Carbon
- GPT-5-Codex vs GPT-6 Astra
- Claude Fable 5.2 vs GPT-5 Pro
- Claude Fable 5.2 vs Claude Fable 5.1
Rankings: Best LLM overall · Best LLM for agents · Best cheap LLMs
Claude Fable 5.2 vs GPT-5-Codex: questions
- Is Claude Fable 5.2 better than GPT-5-Codex?
- Claude Fable 5.2 (max) leads on quality: 69.4 vs 58.6. The BenchLeader Index combines every independent quality benchmark; Claude Fable 5.2 (max) is ahead overall as of 9 Nov 2026, but check the category scores for your use.
- Claude Fable 5.2 vs GPT-5-Codex: which is cheaper, and will that change?
- GPT-5-Codex is cheaper today: $3.44 against $16.00 per million tokens, blended at three input tokens per output token. No price change has been announced for either.
- Is Claude Fable 5.2 or GPT-5-Codex better for coding?
- Claude Fable 5.2 scores higher in coding (80 vs 59 on the category index, where 50 is average).
- Which is faster, Claude Fable 5.2 or GPT-5-Codex?
- GPT-5-Codex streams ~1.5× faster (128 vs 72 output tokens per second, Artificial Analysis 7-day median).
- Which is better for long documents?
- Claude Fable 5.2 scores higher on long-context benchmarks (66 vs 60). Claude Fable 5.2 accepts more context: 1,000,000 against 400,000 tokens for GPT-5-Codex.
More questions (3)
- Which has lower latency?
- Claude Fable 5.2 answers first: 3.0 s against 9.9 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.
- Do independent benchmarks agree on Claude Fable 5.2 vs GPT-5-Codex?
- Across the 3 benchmarks both have, Claude Fable 5.2 leads 3, GPT-5-Codex leads 0. Every overall index that rates both puts Claude Fable 5.2 ahead.
- When was Claude Fable 5.2 released?
- Claude Fable 5.2 was released on 9 Nov 2026.