Verified November 2026

Home › Compare › Claude Fable 5.1 vs Mistral Large

Claude Fable 5.1 vs Mistral Large

A straight cost comparison of Claude Fable 5.1 (Anthropic) and Mistral Large (Mistral AI), per 1,000,000 tokens, verified November 2026.

$10Claude Fable 5.1 input / 1M
$2Mistral Large input / 1M
84%Blended price gap
Mistral LargeCheaper blended

Rate-by-rate comparison

USD per 1M tokens · November 2026
MetricClaude Fable 5.1Mistral LargeWinner
Input / 1M$10$2Mistral Large
Cached input / 1M$0.25$0.20Mistral Large
Cache write / 1M$12.50—Mistral Large
Output / 1M$50$6Mistral Large
Context window1,000,000128,000Claude Fable 5.1
Batch APIYesYesTie
Long-context tierNoneNone—

For per-token metrics, the lower number wins. For context window and batch support, the higher or broader option wins. A dash means the provider does not publish that rate: a missing cache-write fee means writes are not billed per token, while a missing cached-input rate means cache hits bill at the full input rate.

Blended cost: 1M input + 250K output

ModelBlended costDifference
Claude Fable 5.1$22.50+$19
Mistral Large$3.50Cheaper

Ten workloads, two bills

The same two models, priced across ten workloads that differ only in their input:output ratio. Nothing else changes — no retries, no system prompt, no middleware.

WorkloadClaude Fable 5.1Mistral LargeWinner
Classification
100K reqs · 500 in / 10 out
$550$106Mistral Large
Data extraction
10K reqs · 2K in / 200 out
$300$52Mistral Large
Support chat
1K reqs · 4K in / 600 out
$70$11.60Mistral Large
Code review
2K reqs · 8K in / 1.5K out
$310$50Mistral Large
Summarisation
1K reqs · 20K in / 1K out
$250$46Mistral Large
Agent loop
5K reqs · 12K in / 2K out
$1,100$180Mistral Large
Translation
500 reqs · 15K in / 15K out
$450$60Mistral Large
Long-document QA
200 reqs · 120K in / 800 out
$248$48.96Mistral Large
Cached RAG
1K reqs · 50K in (45K cached) / 1K out
$112$25.08Mistral Large
Nightly batch
10K reqs · 10K in / 2K out
$1,000$160Mistral Large

Claude Fable 5.1 wins 0 of 10, Mistral Large wins 10 of 10. The winner is set by the token mix, not by the headline rate.

Where the two bills cross over

Mistral Large is cheaper on both sides of the mix: $2 vs $10 per million input tokens, and $6 vs $50 per million output. No input:output ratio reverses that, so on price alone the other model never becomes the better choice — it has to win on context, latency or output quality.

Cost at four scales

The same support-chat workload (4K input, 600 output per request) at four monthly volumes.

Requests / monthClaude Fable 5.1Mistral LargeDifference
100$7$1.16Mistral Large cheaper by $5.84
1,000$70$11.60Mistral Large cheaper by $58.40
10,000$700$116Mistral Large cheaper by $584
100,000$7,000$1,160Mistral Large cheaper by $5,840

What caching does to each bill

Caching only helps the part of the prompt that repeats. This is the support-chat workload (1,000 requests, 4,000 input tokens each) with an increasing share of that prompt served from a warm cache, written once on the first request.

Cached share of the promptClaude Fable 5.1Mistral LargeWinner
0%$70$11.60Mistral Large
50%$50.52$8Mistral Large
80%$38.84$5.85Mistral Large
95%$33$4.77Mistral Large

Both models publish a cached-read rate, so the discount is comparable: Claude Fable 5.1 reads cache at $0.25 per million, Mistral Large at $0.20.

Which one should you pick?

Claude Fable 5.1 wins on none of the ten workloads above, while Mistral Large wins on classification, data extraction, support chat, code review, summarisation, agent loop, translation, long-document qa, cached rag, nightly batch.

Beyond price, the two differ on input rate ($10 vs $2); output rate ($50 vs $6); cached-input rate ($0.25 vs $0.20); cache-write rate ($12.50 vs —); context window (1,000,000 vs 128,000).

Batch mode is available on Claude Fable 5.1 and Mistral Large, which halves the nightly job. Both publish cached-input discounts, so a stable system prompt is worth caching on either.

FAQ

Is Claude Fable 5.1 cheaper than Mistral Large?

On a blended 1M-input-plus-250K-output workload, Mistral Large is cheaper by 84% ($3.50 vs $22.50). The gap changes for cache-heavy prompts and for batch jobs, as the workload table shows.

Which handles longer prompts, Claude Fable 5.1 or Mistral Large?

Mistral Large has the larger context window (1,000,000 vs 128,000 tokens). Neither model publishes a long-context surcharge.

Do both models support prompt caching?

Claude Fable 5.1 bills cached reads at $0.25 per 1M; Mistral Large at $0.20.

Related comparisons

Run your own token volumes