// head_to_head
Claude Fable 5.1 vs GPT-5.4 Image 2
Published LiveBench scores across all seven categories, live list pricing, context windows, and the measured cost of a point of capability — for both models, side by side.
GPT-5.4 Image 2 is the cheaper of the two; neither can be ranked on quality here.
GPT-5.4 Image 2 does not have a published LiveBench run, so this comparison covers price, context and declared capabilities only. A missing score means "not evaluated", not "worse" — the right way to separate these two is an eval on your own workload.
anthropic
Claude Fable 5.1
- Blended / 1M
- $20.00
- Context
- 1M
- Released
- Sep 1, 2026
- Overall score
- 83.4
openai
GPT-5.4 Image 2
- Blended / 1M
- $9.75
- Context
- 272K
- Released
- Apr 21, 2026
- Overall score
- Not evaluated
Specs and pricing
| Metric | Claude Fable 5.1 | GPT-5.4 Image 2 |
|---|---|---|
| LiveBench overall Mean of the seven LiveBench category scores, 0–100. Higher is better. | 83.4 | — |
| Cost per point Measured benchmark spend divided by overall score — dollars per point of capability. | $0.6668 | — |
| Blended price / 1M 3:1 input:output mix, the usual shape of production traffic. | $20.00 | $9.75win |
| Input price / 1M | $10.00 | $8.00win |
| Output price / 1M | $50.00 | $15.00win |
| Cached input / 1M Price of an input token served from the prompt cache, where the provider publishes one. | $0.250win | $2.00 |
| Context window | 1Mwin | 272K |
| Max output tokens | 128K | 128K |
Benchmarks by category
An overall score averages away the thing you probably care about. These are the seven LiveBench categories scored separately — Claude Fable 5.1 on top, GPT-5.4 Image 2 below, both out of 100.
What each one costs to run
Per-token prices are hard to feel. These are monthly list costs for both models across five workload shapes, using each provider's published cached-input rate where there is one.
| Workload | Claude Fable 5.1 | GPT-5.4 Image 2 |
|---|---|---|
| Support chatbot 1.2K in / 400 out × 200K requests | $5,698/mo | $2,688/mo |
| RAG assistant 8K in / 600 out × 100K requests | $7,100/mo | $4,900/mo |
| Coding agent 40K in / 4K out × 20K requests | $6,540/mo | $4,240/mo |
| Document extraction 20K in / 1.5K out × 50K requests | $13,263/mo | $8,825/mo |
| Bulk classification 500 in / 20 out × 5M requests | $25,125/mo | $18,500/mo |
Which should you pick?
You need to fit large documents in one call
Claude Fable 5.1
Wider context window — 1M against 272K.
You are cost-constrained
GPT-5.4 Image 2
Cheaper on blended list price at $9.75 per million tokens.
Claude Fable 5.1 vs GPT-5.4 Image 2 FAQ
Which is better, Claude Fable 5.1 or GPT-5.4 Image 2?
GPT-5.4 Image 2 is the cheaper of the two; neither can be ranked on quality here. GPT-5.4 Image 2 does not have a published LiveBench run, so this comparison covers price, context and declared capabilities only. A missing score means "not evaluated", not "worse" — the right way to separate these two is an eval on your own workload.
Is Claude Fable 5.1 cheaper than GPT-5.4 Image 2?
GPT-5.4 Image 2 is cheaper. On a 3:1 input:output blend, Claude Fable 5.1 lists at $20.00 per million tokens and GPT-5.4 Image 2 at $9.75 — GPT-5.4 Image 2 is 2.1× cheaper. Input and output are priced separately — Claude Fable 5.1 charges $10.00 in and $50.00 out, GPT-5.4 Image 2 charges $8.00 and $15.00 — so the model that looks cheaper flips depending on how output-heavy your workload is.
Does Claude Fable 5.1 or GPT-5.4 Image 2 have a bigger context window?
Claude Fable 5.1 has the larger context window: 1M for Claude Fable 5.1 against 272K for GPT-5.4 Image 2. Note that a window you can fill is not a window you should fill — retrieval quality usually degrades well before the limit, and you pay for every token you put in it.
Do Claude Fable 5.1 and GPT-5.4 Image 2 support prompt caching?
Both publish a cached-input rate: $0.250 per million for Claude Fable 5.1 and $2.00 for GPT-5.4 Image 2, against full input rates of $10.00 and $8.00. On a workload with a long stable prefix — a system prompt, a tool schema, a retrieved corpus — that changes the economics more than the headline price does.
Related comparisons
How these numbers are produced
- Price — provider list price from OpenRouter, refreshed every 15 minutes. “Blended” is a 3:1 input:output mix.
- Scores — LiveBench release 2026-06-25, using their own category map. Each model shows its strongest published run. A blank means “not evaluated”, never “bad”.
- Cost per point — the measured dollars LiveBench spent on the run, divided by the score it earned.
- “Win” — awarded only past a threshold: one full point on a benchmark score, 10% on a price, 25% on a context window. Anything tighter reports as a tie, because effort settings alone move a LiveBench score by more than that.
Published benchmarks rank models on someone else's tasks. Before committing, see LLM & agent evaluation for building an eval on your own.