GPT-5.3 Codex vs Claude Fable 5
Head-to-head specifications
| Metric | GPT-5.3 Codex | Claude Fable 5 | Difference |
|---|---|---|---|
| Intelligence Index | 44.0 | 60.0 | -26.7% |
| Context window | 922K tokens | 1M tokens | — |
| Blended price ($/1M tokens) | $1.05 | $1.68 | -37.5% |
| Output speed (tokens/s) | 85 | 65 | +30.8% |
| Access | Proprietary API | Proprietary API | — |
- Claude Fable 5 leads overall capability (Intelligence Index 60.0 vs 44.0).
- GPT-5.3 Codex is the cheaper model to run at $1.05/1M blended tokens — about 1.6× cheaper.
- Claude Fable 5 offers the larger context window (1M tokens), useful for long documents and codebases.
Verdict: GPT-5.3 Codex or Claude Fable 5?
GPT-5.3 Codex advantages
- Affordability (+38%)
- Output speed (+24%)
Claude Fable 5 advantages
- General intelligence (+27%)
- Context window (+8%)
Which should you choose?
- Choose the GPT-5.3 Codex if you want the lowest cost per token at scale.
- Choose the Claude Fable 5 if you need the strongest overall reasoning and accuracy.
- Choose the GPT-5.3 Codex if low latency and fast generation matter for your application.
Value for money
GPT-5.3 Codex offers more intelligence per dollar (1.2× the Intelligence-Index-per-cost of the alternative), making it the stronger value for high-volume use.
GPT-5.3 Codex vs Claude Fable 5: which should you choose?
GPT-5.3 Codex — OpenAI multimodal model with an Intelligence Index of 44, a 922K-token context window and a blended price of $1.05/1M tokens.
Claude Fable 5 — Anthropic multimodal model with an Intelligence Index of 60, a 1M-token context window and a blended price of $1.68/1M tokens.
GPT-5.3 Codex vs Claude Fable 5: Claude Fable 5 scores higher on the Intelligence Index. Claude Fable 5 leads overall capability (Intelligence Index 60.0 vs 44.0). GPT-5.3 Codex is the cheaper model to run at $1.05/1M blended tokens — about 1.6× cheaper.
Capability: intelligence, coding and agentic work
On the composite Intelligence Index the Claude Fable 5 scores 60.0 versus 44.0. Composite indices summarize many evaluations, but always test on your own workload before committing.
Context window and speed
The Claude Fable 5 accepts up to 1 million tokens per request, which sets how much documentation, transcript or code it can reason over at once. In measured throughput, GPT-5.3 Codex generates faster (85 vs 65 tokens/s), which matters for interactive apps and high-volume pipelines.
Pricing and access
At blended per-token rates, GPT-5.3 Codex is the cheaper model to run ($1.05 vs $1.68 per 1M tokens). GPT-5.3 Codex is proprietary api and Claude Fable 5 is proprietary api. Open-weight models can be self-hosted, trading per-call cost for infrastructure you manage; for production also weigh rate limits, throughput and data-residency requirements.
The verdict
Both are credible choices in the ai model comparison space; the specification table above lays out every metric so you can weigh the trade-offs that matter to you. Pick the one whose strengths line up with how you will actually use it.
Frequently asked questions
Is the GPT-5.3 Codex better than the Claude Fable 5?
These two are closely matched — the right pick comes down to which specific strengths you value and the price you actually pay. Claude Fable 5 leads overall capability (Intelligence Index 60.0 vs 44.0).
What is the main difference between the GPT-5.3 Codex and the Claude Fable 5?
Claude Fable 5 leads overall capability (Intelligence Index 60.0 vs 44.0). GPT-5.3 Codex is the cheaper model to run at $1.05/1M blended tokens — about 1.6× cheaper.
Which is better value?
GPT-5.3 Codex offers more intelligence per dollar (1.2× the Intelligence-Index-per-cost of the alternative), making it the stronger value for high-volume use.
Which should I choose?
Choose the GPT-5.3 Codex if you want the lowest cost per token at scale. Choose the Claude Fable 5 if you need the strongest overall reasoning and accuracy.
Methodology
Large language models are compared on independent leaderboard metrics: an Intelligence Index (a composite of reasoning and knowledge evaluations), Coding and Agentic indices where measured, community arena Elo, maximum context window, a blended API price per million tokens (weighted across cache-hit, input and output rates), and measured output speed in tokens per second. Where a model ships multiple reasoning-effort variants, we report its strongest variant. Benchmarks capture only part of real-world quality, which also depends on tool use, latency, safety and task fit — and this space moves quickly, so figures reflect the leaderboard snapshot on the page date.