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Claude Sonnet 5.5 vs Qwen3.7 Max

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, Qwen3.7 Max costs less — it depends on your workload.

Claude Sonnet 5.5 is ahead by 4.6 points overall, and Qwen3.7 Max lists 1.8× cheaper per blended million tokens. Whether 4.6 points is worth that depends on how much a wrong answer costs you. The two cost measures disagree here, which is worth knowing: Qwen3.7 Max has the lower sticker price, but Claude Sonnet 5.5 earns each point of capability for less — $0.0742 against $0.0971 — because per-token rates do not predict how many tokens a model actually spends on a task.

anthropic

Claude Sonnet 5.5

Blended / 1M
$4.00
Context
1M
Released
Sep 28, 2026
Overall score
77.8
reasoningtool callingimage inputfile inputprompt caching

qwen

Qwen3.7 Max

Blended / 1M
$2.21
Context
1M
Released
May 21, 2026
Overall score
73.1
reasoningtool callingprompt caching

Specs and pricing

MetricClaude Sonnet 5.5Qwen3.7 Max
LiveBench overall

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

77.8win73.1
Cost per point

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

$0.0742win$0.0971
Blended price / 1M

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

$4.00$2.21win
Input price / 1M$2.00$1.48win
Output price / 1M$10.00$4.42win
Cached input / 1M

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

$0.200win$0.295
Context window1M1M
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 Sonnet 5.5 on top, Qwen3.7 Max below, both out of 100.

Agentic coding
39.3
43.6
Coding
88.9
74.2
Reasoning
86.8
83.3
Mathematics
96.7
85.2
Data analysis
78.6
71.8
Language
83.4
79.7
Instruction following
70.5
74.0

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 Sonnet 5.5Qwen3.7 Max
Support chatbot

1.2K in / 400 out × 200K requests

$1,150/mo$623.04/mo
RAG assistant

8K in / 600 out × 100K requests

$1,480/mo$973.50/mo
Coding agent

40K in / 4K out × 20K requests

$1,392/mo$873.20/mo
Document extraction

20K in / 1.5K out × 50K requests

$2,660/mo$1,748/mo
Bulk classification

500 in / 20 out × 5M requests

$5,100/mo$3,540/mo
Run these two through the cost calculator

Which should you pick?

You are running this at volume

Claude Sonnet 5.5

Lowest measured cost per point of capability at $0.0742 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 77.8.

The workload is coding or agentic work

Qwen3.7 Max

Leads on agentic coding — 43.6 against 39.3.

Claude Sonnet 5.5 vs Qwen3.7 Max FAQ

Which is better, Claude Sonnet 5.5 or Qwen3.7 Max?

Claude Sonnet 5.5 scores higher, Qwen3.7 Max costs less — it depends on your workload. Claude Sonnet 5.5 is ahead by 4.6 points overall, and Qwen3.7 Max lists 1.8× cheaper per blended million tokens. Whether 4.6 points is worth that depends on how much a wrong answer costs you. The two cost measures disagree here, which is worth knowing: Qwen3.7 Max has the lower sticker price, but Claude Sonnet 5.5 earns each point of capability for less — $0.0742 against $0.0971 — because per-token rates do not predict how many tokens a model actually spends on a task.

Is Claude Sonnet 5.5 cheaper than Qwen3.7 Max?

Qwen3.7 Max is cheaper. On a 3:1 input:output blend, Claude Sonnet 5.5 lists at $4.00 per million tokens and Qwen3.7 Max at $2.21 — Qwen3.7 Max is 1.8× cheaper. Input and output are priced separately — Claude Sonnet 5.5 charges $2.00 in and $10.00 out, Qwen3.7 Max charges $1.48 and $4.42 — so the model that looks cheaper flips depending on how output-heavy your workload is.

Claude Sonnet 5.5 vs Qwen3.7 Max: which scores higher on benchmarks?

Claude Sonnet 5.5 scores 77.8 and Qwen3.7 Max scores 73.1 overall on LiveBench, the mean of its seven categories. That is a 4.6-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 Qwen3.7 Max?

Claude Sonnet 5.5. 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 $0.0742 per point and Qwen3.7 Max at $0.0971.

Does Claude Sonnet 5.5 or Qwen3.7 Max have a bigger context window?

They are effectively the same — 1M for Claude Sonnet 5.5 and 1M for Qwen3.7 Max.

Do Claude Sonnet 5.5 and Qwen3.7 Max support prompt caching?

Both publish a cached-input rate: $0.200 per million for Claude Sonnet 5.5 and $0.295 for Qwen3.7 Max, against full input rates of $2.00 and $1.48. 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.