// head_to_head
Claude Fable 5 vs Qwen2.5 VL 72B Instruct
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.
Qwen2.5 VL 72B Instruct is the cheaper of the two; neither can be ranked on quality here.
Qwen2.5 VL 72B Instruct 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
- Blended / 1M
- $20.00
- Context
- 1M
- Released
- Jun 9, 2026
- Overall score
- 83.0
qwen
Qwen2.5 VL 72B Instruct
- Blended / 1M
- $0.850
- Context
- 128K
- Released
- Feb 1, 2025
- Overall score
- Not evaluated
Specs and pricing
| Metric | Claude Fable 5 | Qwen2.5 VL 72B Instruct |
|---|---|---|
| LiveBench overall Mean of the seven LiveBench category scores, 0–100. Higher is better. | 83.0 | — |
| Cost per point Measured benchmark spend divided by overall score — dollars per point of capability. | $0.7925 | — |
| Blended price / 1M 3:1 input:output mix, the usual shape of production traffic. | $20.00 | $0.850win |
| Input price / 1M | $10.00 | $0.800win |
| Output price / 1M | $50.00 | $1.00win |
| Cached input / 1M Price of an input token served from the prompt cache, where the provider publishes one. | $1.00 | $0.400win |
| Context window | 1Mwin | 128K |
| Max output tokens | 128K | 115K |
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 on top, Qwen2.5 VL 72B Instruct 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 | Qwen2.5 VL 72B Instruct |
|---|---|---|
| Support chatbot 1.2K in / 400 out × 200K requests | $5,752/mo | $243.20/mo |
| RAG assistant 8K in / 600 out × 100K requests | $7,400/mo | $540.00/mo |
| Coding agent 40K in / 4K out × 20K requests | $6,960/mo | $496.00/mo |
| Document extraction 20K in / 1.5K out × 50K requests | $13,300/mo | $855.00/mo |
| Bulk classification 500 in / 20 out × 5M requests | $25,500/mo | $1,900/mo |
Which should you pick?
You need to fit large documents in one call
Claude Fable 5
Wider context window — 1M against 128K.
You are cost-constrained
Qwen2.5 VL 72B Instruct
Cheaper on blended list price at $0.850 per million tokens.
Claude Fable 5 vs Qwen2.5 VL 72B Instruct FAQ
Which is better, Claude Fable 5 or Qwen2.5 VL 72B Instruct?
Qwen2.5 VL 72B Instruct is the cheaper of the two; neither can be ranked on quality here. Qwen2.5 VL 72B Instruct 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 cheaper than Qwen2.5 VL 72B Instruct?
Qwen2.5 VL 72B Instruct is cheaper. On a 3:1 input:output blend, Claude Fable 5 lists at $20.00 per million tokens and Qwen2.5 VL 72B Instruct at $0.850 — Qwen2.5 VL 72B Instruct is 24× cheaper. Input and output are priced separately — Claude Fable 5 charges $10.00 in and $50.00 out, Qwen2.5 VL 72B Instruct charges $0.800 and $1.00 — so the model that looks cheaper flips depending on how output-heavy your workload is.
Does Claude Fable 5 or Qwen2.5 VL 72B Instruct have a bigger context window?
Claude Fable 5 has the larger context window: 1M for Claude Fable 5 against 128K for Qwen2.5 VL 72B Instruct. 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 and Qwen2.5 VL 72B Instruct support prompt caching?
Both publish a cached-input rate: $1.00 per million for Claude Fable 5 and $0.400 for Qwen2.5 VL 72B Instruct, against full input rates of $10.00 and $0.800. 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.