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Claude Fable 5.1 vs Gemini 3.5 Flash Lite

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

Claude Fable 5.1 scores higher, Gemini 3.5 Flash Lite costs less — it depends on your workload.

Claude Fable 5.1 is ahead by 19.5 points overall, and Gemini 3.5 Flash Lite lists 24× cheaper per blended million tokens. Whether 19.5 points is worth that depends on how much a wrong answer costs you. Gemini 3.5 Flash Lite also leads on measured cost per point of capability, at $0.0379 per point.

anthropic

Claude Fable 5.1

Blended / 1M
$20.00
Context
1M
Released
Sep 1, 2026
Overall score
83.4
reasoningtool callingimage inputfile inputprompt caching

google

Gemini 3.5 Flash Lite

Blended / 1M
$0.850
Context
1.0M
Released
Jul 21, 2026
Overall score
63.9
reasoningtool callingimage inputvideo inputfile inputaudio inputprompt caching

Specs and pricing

MetricClaude Fable 5.1Gemini 3.5 Flash Lite
LiveBench overall

Mean of the seven LiveBench category scores, 0–100. Higher is better.

83.4win63.9
Cost per point

Measured benchmark spend divided by overall score — dollars per point of capability.

$0.6668$0.0379win
Blended price / 1M

3:1 input:output mix, the usual shape of production traffic.

$20.00$0.850win
Input price / 1M$10.00$0.300win
Output price / 1M$50.00$2.50win
Cached input / 1M

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

$0.250$0.030win
Context window1M1.0M
Max output tokens128Kwin66K

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, Gemini 3.5 Flash Lite below, both out of 100.

Agentic coding
66.1
45.3
Coding
86.4
76.1
Reasoning
91.7
60.2
Mathematics
97.0
73.7
Data analysis
80.3
53.2
Language
89.5
71.8
Instruction following
73.0
67.2

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 5.1Gemini 3.5 Flash Lite
Support chatbot

1.2K in / 400 out × 200K requests

$5,698/mo$252.56/mo
RAG assistant

8K in / 600 out × 100K requests

$7,100/mo$282.00/mo
Coding agent

40K in / 4K out × 20K requests

$6,540/mo$288.80/mo
Document extraction

20K in / 1.5K out × 50K requests

$13,263/mo$474.00/mo
Bulk classification

500 in / 20 out × 5M requests

$25,125/mo$865.00/mo
Run these two through the cost calculator

Which should you pick?

You are running this at volume

Gemini 3.5 Flash Lite

Lowest measured cost per point of capability at $0.0379 per point — the gap compounds with every request.

Quality matters more than the bill

Claude Fable 5.1

Highest overall LiveBench score of the two at 83.4.

The workload is coding or agentic work

Claude Fable 5.1

Leads on agentic coding — 66.1 against 45.3.

Claude Fable 5.1 vs Gemini 3.5 Flash Lite FAQ

Which is better, Claude Fable 5.1 or Gemini 3.5 Flash Lite?

Claude Fable 5.1 scores higher, Gemini 3.5 Flash Lite costs less — it depends on your workload. Claude Fable 5.1 is ahead by 19.5 points overall, and Gemini 3.5 Flash Lite lists 24× cheaper per blended million tokens. Whether 19.5 points is worth that depends on how much a wrong answer costs you. Gemini 3.5 Flash Lite also leads on measured cost per point of capability, at $0.0379 per point.

Is Claude Fable 5.1 cheaper than Gemini 3.5 Flash Lite?

Gemini 3.5 Flash Lite is cheaper. On a 3:1 input:output blend, Claude Fable 5.1 lists at $20.00 per million tokens and Gemini 3.5 Flash Lite at $0.850 — Gemini 3.5 Flash Lite is 24× cheaper. Input and output are priced separately — Claude Fable 5.1 charges $10.00 in and $50.00 out, Gemini 3.5 Flash Lite charges $0.300 and $2.50 — so the model that looks cheaper flips depending on how output-heavy your workload is.

Claude Fable 5.1 vs Gemini 3.5 Flash Lite: which scores higher on benchmarks?

Claude Fable 5.1 scores 83.4 and Gemini 3.5 Flash Lite scores 63.9 overall on LiveBench, the mean of its seven categories. That is a 19.5-point lead for Claude Fable 5.1. Category scores differ from the overall figure — a model can lead on reasoning and trail on coding, which the per-category table above breaks out.

Which gives better value for money, Claude Fable 5.1 or Gemini 3.5 Flash Lite?

Gemini 3.5 Flash Lite. Cost per point divides the measured dollars LiveBench spent running the benchmark by the score it earned, so it captures something token pricing misses: a reasoning model can emit many times more tokens than its per-token rate suggests. Claude Fable 5.1 works out at $0.6668 per point and Gemini 3.5 Flash Lite at $0.0379.

Does Claude Fable 5.1 or Gemini 3.5 Flash Lite have a bigger context window?

They are effectively the same — 1M for Claude Fable 5.1 and 1.0M for Gemini 3.5 Flash Lite.

Do Claude Fable 5.1 and Gemini 3.5 Flash Lite support prompt caching?

Both publish a cached-input rate: $0.250 per million for Claude Fable 5.1 and $0.030 for Gemini 3.5 Flash Lite, 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.