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Claude Sonnet 5.5 vs Gemma 4 31B

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 Sonnet 5.5 scores higher, Gemma 4 31B costs less — it depends on your workload.

Claude Sonnet 5.5 is ahead by 41.3 points overall, and Gemma 4 31B lists 16× cheaper per blended million tokens. Whether 41.3 points is worth that depends on how much a wrong answer costs you. Gemma 4 31B also leads on measured cost per point of capability, at $0.0017 per point.

anthropic

Claude Sonnet 5.5

Blended / 1M
$4.00
Context
1M
Released
Sep 28, 2026
Overall score
80.0
reasoningtool callingimage inputfile inputprompt caching

google

Gemma 4 31B

Blended / 1M
$0.250
Context
262K
Released
Apr 2, 2026
Overall score
38.7
reasoningtool callingimage inputvideo input

Specs and pricing

MetricClaude Sonnet 5.5Gemma 4 31B
LiveBench overall

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

80.0win38.7
Cost per point

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

$0.0372$0.0017win
Blended price / 1M

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

$4.00$0.250win
Input price / 1M$2.00$0.200win
Output price / 1M$10.00$0.400win
Cached input / 1M

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

$0.100—
Context window1Mwin262K
Max output tokens128Kwin16K

Benchmarks by category

An overall score averages away the thing you probably care about. These are the seven LiveBench categories scored separately — Claude Sonnet 5.5 on top, Gemma 4 31B below, both out of 100.

Long-horizon agentic
63.4
10.7
Coding
94.7
49.0
Reasoning
91.6
68.3
Browser
89.3
0.0
Data analysis
59.5
53.3
Research
83.6
44.4
Documents & design
85.0
58.4

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 Sonnet 5.5Gemma 4 31B
Support chatbot

1.2K in / 400 out × 200K requests

$1,143/mo$80.00/mo
RAG assistant

8K in / 600 out × 100K requests

$1,440/mo$184.00/mo
Coding agent

40K in / 4K out × 20K requests

$1,336/mo$192.00/mo
Document extraction

20K in / 1.5K out × 50K requests

$2,655/mo$230.00/mo
Bulk classification

500 in / 20 out × 5M requests

$5,050/mo$540.00/mo
Run these two through the cost calculator

Which should you pick?

You are running this at volume

Gemma 4 31B

Lowest measured cost per point of capability at $0.0017 per point — the gap compounds with every request.

Quality matters more than the bill

Claude Sonnet 5.5

Highest overall LiveBench score of the two at 80.0.

The workload is coding or agentic work

Claude Sonnet 5.5

Leads on long-horizon agentic — 63.4 against 10.7.

You need to fit large documents in one call

Claude Sonnet 5.5

Wider context window — 1M against 262K.

Claude Sonnet 5.5 vs Gemma 4 31B FAQ

Which is better, Claude Sonnet 5.5 or Gemma 4 31B?

Claude Sonnet 5.5 scores higher, Gemma 4 31B costs less — it depends on your workload. Claude Sonnet 5.5 is ahead by 41.3 points overall, and Gemma 4 31B lists 16× cheaper per blended million tokens. Whether 41.3 points is worth that depends on how much a wrong answer costs you. Gemma 4 31B also leads on measured cost per point of capability, at $0.0017 per point.

Is Claude Sonnet 5.5 cheaper than Gemma 4 31B?

Gemma 4 31B is cheaper. On a 3:1 input:output blend, Claude Sonnet 5.5 lists at $4.00 per million tokens and Gemma 4 31B at $0.250 — Gemma 4 31B is 16× cheaper. Input and output are priced separately — Claude Sonnet 5.5 charges $2.00 in and $10.00 out, Gemma 4 31B charges $0.200 and $0.400 — so the model that looks cheaper flips depending on how output-heavy your workload is.

Claude Sonnet 5.5 vs Gemma 4 31B: which scores higher on benchmarks?

Claude Sonnet 5.5 scores 80.0 and Gemma 4 31B scores 38.7 overall on LiveBench, the weighted mean of its seven categories. That is a 41.3-point lead for Claude Sonnet 5.5. 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 Sonnet 5.5 or Gemma 4 31B?

Gemma 4 31B. 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 Sonnet 5.5 works out at $0.0372 per point and Gemma 4 31B at $0.0017.

Does Claude Sonnet 5.5 or Gemma 4 31B have a bigger context window?

Claude Sonnet 5.5 has the larger context window: 1M for Claude Sonnet 5.5 against 262K for Gemma 4 31B. 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 Sonnet 5.5 and Gemma 4 31B support prompt caching?

Claude Sonnet 5.5 publishes a cached-input rate of $0.100 per million tokens against a full input rate of $2.00. The catalogue lists no separate cached rate for Gemma 4 31B, which means the provider does not price it separately here — not that caching is unavailable.

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 2026-10-25, 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.