// head_to_head

Claude Fable 5.1 vs MiniMax M2.7

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.

OpenRouter + LiveBenchAll comparisonsFull leaderboard

MiniMax M2.7 is the cheaper of the two; neither can be ranked on quality here.

MiniMax M2.7 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
reasoningtool callingimage inputfile inputprompt caching

minimax

MiniMax M2.7

Blended / 1M
$0.525
Context
205K
Released
Mar 18, 2026
Overall score
Not evaluated
reasoningtool callingprompt caching

Specs and pricing

MetricClaude Fable 5.1MiniMax M2.7
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$0.525win
Input price / 1M$10.00$0.300win
Output price / 1M$50.00$1.20win
Cached input / 1M

Price of an input token served from the prompt cache, where the provider publishes one.

$0.250$0.060win
Context window1Mwin205K
Max output tokens128K131K

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, MiniMax M2.7 below, both out of 100.

Agentic coding
66.1
Coding
86.4
Reasoning
91.7
Mathematics
97.0
Data analysis
80.3
Language
89.5
Instruction following
73.0

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.

WorkloadClaude Fable 5.1MiniMax M2.7
Support chatbot

1.2K in / 400 out × 200K requests

$5,698/mo$150.72/mo
RAG assistant

8K in / 600 out × 100K requests

$7,100/mo$216.00/mo
Coding agent

40K in / 4K out × 20K requests

$6,540/mo$201.60/mo
Document extraction

20K in / 1.5K out × 50K requests

$13,263/mo$378.00/mo
Bulk classification

500 in / 20 out × 5M requests

$25,125/mo$750.00/mo
Run these two through the cost calculator

Which should you pick?

You need to fit large documents in one call

Claude Fable 5.1

Wider context window — 1M against 205K.

You are cost-constrained

MiniMax M2.7

Cheaper on blended list price at $0.525 per million tokens.

Claude Fable 5.1 vs MiniMax M2.7 FAQ

Which is better, Claude Fable 5.1 or MiniMax M2.7?

MiniMax M2.7 is the cheaper of the two; neither can be ranked on quality here. MiniMax M2.7 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 MiniMax M2.7?

MiniMax M2.7 is cheaper. On a 3:1 input:output blend, Claude Fable 5.1 lists at $20.00 per million tokens and MiniMax M2.7 at $0.525 — MiniMax M2.7 is 38× cheaper. Input and output are priced separately — Claude Fable 5.1 charges $10.00 in and $50.00 out, MiniMax M2.7 charges $0.300 and $1.20 — so the model that looks cheaper flips depending on how output-heavy your workload is.

Does Claude Fable 5.1 or MiniMax M2.7 have a bigger context window?

Claude Fable 5.1 has the larger context window: 1M for Claude Fable 5.1 against 205K for MiniMax M2.7. 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 MiniMax M2.7 support prompt caching?

Both publish a cached-input rate: $0.250 per million for Claude Fable 5.1 and $0.060 for MiniMax M2.7, against full input rates of $10.00 and $0.300. 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.
  • ScoresLiveBench 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.