// head_to_head
GPT-5.4 Mini 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.
Qwen3.7 Max scores higher, GPT-5.4 Mini costs less — it depends on your workload.
Qwen3.7 Max is ahead by 6.8 points overall, and GPT-5.4 Mini lists 31% cheaper per blended million tokens. Whether 6.8 points is worth that depends on how much a wrong answer costs you. The two cost measures disagree here, which is worth knowing: GPT-5.4 Mini has the lower sticker price, but Qwen3.7 Max earns each point of capability for less — $0.0971 against $0.1871 — because per-token rates do not predict how many tokens a model actually spends on a task.
openai
GPT-5.4 Mini
- Blended / 1M
- $1.69
- Context
- 400K
- Released
- Mar 17, 2026
- Overall score
- 66.4
qwen
Qwen3.7 Max
- Blended / 1M
- $2.21
- Context
- 1M
- Released
- May 21, 2026
- Overall score
- 73.1
Specs and pricing
| Metric | GPT-5.4 Mini | Qwen3.7 Max |
|---|---|---|
| LiveBench overall Mean of the seven LiveBench category scores, 0–100. Higher is better. | 66.4 | 73.1win |
| Cost per point Measured benchmark spend divided by overall score — dollars per point of capability. | $0.1871 | $0.0971win |
| Blended price / 1M 3:1 input:output mix, the usual shape of production traffic. | $1.69win | $2.21 |
| Input price / 1M | $0.750win | $1.48 |
| Output price / 1M | $4.50 | $4.42 |
| Cached input / 1M Price of an input token served from the prompt cache, where the provider publishes one. | $0.075win | $0.295 |
| Context window | 400K | 1Mwin |
| Max output tokens | 128K | 131K |
Benchmarks by category
An overall score averages away the thing you probably care about. These are the seven LiveBench categories scored separately — GPT-5.4 Mini on top, Qwen3.7 Max below, both out of 100.
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.
| Workload | GPT-5.4 Mini | Qwen3.7 Max |
|---|---|---|
| Support chatbot 1.2K in / 400 out × 200K requests | $491.40/mo | $623.04/mo |
| RAG assistant 8K in / 600 out × 100K requests | $600.00/mo | $973.50/mo |
| Coding agent 40K in / 4K out × 20K requests | $582.00/mo | $873.20/mo |
| Document extraction 20K in / 1.5K out × 50K requests | $1053.75/mo | $1747.88/mo |
| Bulk classification 500 in / 20 out × 5M requests | $1987.50/mo | $3540.00/mo |
Which should you pick?
You are running this at volume
Qwen3.7 Max
Lowest measured cost per point of capability at $0.0971 per point — the gap compounds with every request.
Quality matters more than the bill
Qwen3.7 Max
Highest overall LiveBench score of the two at 73.1.
The workload is coding or agentic work
Qwen3.7 Max
Leads on agentic coding — 43.6 against 41.7.
You need to fit large documents in one call
Qwen3.7 Max
Wider context window — 1M against 400K.
You are cost-constrained
GPT-5.4 Mini
Cheaper on blended list price at $1.69 per million tokens.
GPT-5.4 Mini vs Qwen3.7 Max FAQ
Which is better, GPT-5.4 Mini or Qwen3.7 Max?
Qwen3.7 Max scores higher, GPT-5.4 Mini costs less — it depends on your workload. Qwen3.7 Max is ahead by 6.8 points overall, and GPT-5.4 Mini lists 31% cheaper per blended million tokens. Whether 6.8 points is worth that depends on how much a wrong answer costs you. The two cost measures disagree here, which is worth knowing: GPT-5.4 Mini has the lower sticker price, but Qwen3.7 Max earns each point of capability for less — $0.0971 against $0.1871 — because per-token rates do not predict how many tokens a model actually spends on a task.
Is GPT-5.4 Mini cheaper than Qwen3.7 Max?
GPT-5.4 Mini is cheaper. On a 3:1 input:output blend, GPT-5.4 Mini lists at $1.69 per million tokens and Qwen3.7 Max at $2.21 — GPT-5.4 Mini is 31% cheaper. Input and output are priced separately — GPT-5.4 Mini charges $0.750 in and $4.50 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.
GPT-5.4 Mini vs Qwen3.7 Max: which scores higher on benchmarks?
GPT-5.4 Mini scores 66.4 and Qwen3.7 Max scores 73.1 overall on LiveBench, the mean of its seven categories. That is a 6.8-point lead for Qwen3.7 Max. 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, GPT-5.4 Mini or Qwen3.7 Max?
Qwen3.7 Max. 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. GPT-5.4 Mini works out at $0.1871 per point and Qwen3.7 Max at $0.0971.
Does GPT-5.4 Mini or Qwen3.7 Max have a bigger context window?
Qwen3.7 Max has the larger context window: 400K for GPT-5.4 Mini against 1M for Qwen3.7 Max. 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 GPT-5.4 Mini and Qwen3.7 Max support prompt caching?
Both publish a cached-input rate: $0.075 per million for GPT-5.4 Mini and $0.295 for Qwen3.7 Max, against full input rates of $0.750 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.