// head_to_head
Qwen3.7 Max vs Inkling
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.
Inkling is the cheaper of the two; neither can be ranked on quality here.
Qwen3.7 Max 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.
qwen
Qwen3.7 Max
- Blended / 1M
- $2.21
- Context
- 1M
- Released
- May 21, 2026
- Overall score
- Not evaluated
thinkingmachines
Inkling
- Blended / 1M
- $1.76
- Context
- 524K
- Released
- Jul 17, 2026
- Overall score
- 47.0
Specs and pricing
| Metric | Qwen3.7 Max | Inkling |
|---|---|---|
| LiveBench overall Weighted mean of the seven LiveBench category scores, 0–100. Higher is better. | — | 47.0 |
| Cost per point Measured benchmark spend divided by overall score — dollars per point of capability. | — | $0.0179 |
| Blended price / 1M 3:1 input:output mix, the usual shape of production traffic. | $2.21 | $1.76win |
| Input price / 1M | $1.48 | $1.00win |
| Output price / 1M | $4.42 | $4.05 |
| Cached input / 1M Price of an input token served from the prompt cache, where the provider publishes one. | $0.295 | $0.170win |
| Context window | 1Mwin | 524K |
| Max output tokens | 131K | 472Kwin |
Benchmarks by category
An overall score averages away the thing you probably care about. These are the seven LiveBench categories scored separately — Qwen3.7 Max on top, Inkling 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 | Qwen3.7 Max | Inkling |
|---|---|---|
| Support chatbot 1.2K in / 400 out × 200K requests | $623.04/mo | $504.24/mo |
| RAG assistant 8K in / 600 out × 100K requests | $973.50/mo | $711.00/mo |
| Coding agent 40K in / 4K out × 20K requests | $873.20/mo | $659.20/mo |
| Document extraction 20K in / 1.5K out × 50K requests | $1,748/mo | $1,262/mo |
| Bulk classification 500 in / 20 out × 5M requests | $3,540/mo | $2,490/mo |
Which should you pick?
You need to fit large documents in one call
Qwen3.7 Max
Wider context window — 1M against 524K.
You are cost-constrained
Inkling
Cheaper on blended list price at $1.76 per million tokens.
Qwen3.7 Max vs Inkling FAQ
Which is better, Qwen3.7 Max or Inkling?
Inkling is the cheaper of the two; neither can be ranked on quality here. Qwen3.7 Max 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 Qwen3.7 Max cheaper than Inkling?
Inkling is cheaper. On a 3:1 input:output blend, Qwen3.7 Max lists at $2.21 per million tokens and Inkling at $1.76 — Inkling is 26% cheaper. Input and output are priced separately — Qwen3.7 Max charges $1.48 in and $4.42 out, Inkling charges $1.00 and $4.05 — so the model that looks cheaper flips depending on how output-heavy your workload is.
Does Qwen3.7 Max or Inkling have a bigger context window?
Qwen3.7 Max has the larger context window: 1M for Qwen3.7 Max against 524K for Inkling. 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 Qwen3.7 Max and Inkling support prompt caching?
Both publish a cached-input rate: $0.295 per million for Qwen3.7 Max and $0.170 for Inkling, against full input rates of $1.48 and $1.00. 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 2026-10-25, 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.