// head_to_head
GPT-6.1 Sol vs Qwen3.8 Flash
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.
GPT-6.1 Sol scores higher, Qwen3.8 Flash costs less — it depends on your workload.
GPT-6.1 Sol is ahead by 38.5 points overall, and Qwen3.8 Flash lists 17× cheaper per blended million tokens. Whether 38.5 points is worth that depends on how much a wrong answer costs you. Qwen3.8 Flash also leads on measured cost per point of capability, at $0.2009 per point.
openai
GPT-6.1 Sol
- Blended / 1M
- $4.00
- Context
- 1.1M
- Released
- Sep 29, 2026
- Overall score
- 85.4
qwen
Qwen3.8 Flash
- Blended / 1M
- $0.230
- Context
- 1M
- Released
- Aug 26, 2026
- Overall score
- 47.0
Specs and pricing
| Metric | GPT-6.1 Sol | Qwen3.8 Flash |
|---|---|---|
| LiveBench overall Mean of the seven LiveBench category scores, 0–100. Higher is better. | 85.4win | 47.0 |
| Cost per point Measured benchmark spend divided by overall score — dollars per point of capability. | $0.4971 | $0.2009win |
| Blended price / 1M 3:1 input:output mix, the usual shape of production traffic. | $4.00 | $0.230win |
| Input price / 1M | $2.00 | $0.150win |
| Output price / 1M | $10.00 | $0.470win |
| Cached input / 1M Price of an input token served from the prompt cache, where the provider publishes one. | $0.100 | $0.016win |
| Context window | 1.1M | 1M |
| 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-6.1 Sol on top, Qwen3.8 Flash 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-6.1 Sol | Qwen3.8 Flash |
|---|---|---|
| Support chatbot 1.2K in / 400 out × 200K requests | $1,143/mo | $63.95/mo |
| RAG assistant 8K in / 600 out × 100K requests | $1,440/mo | $94.60/mo |
| Coding agent 40K in / 4K out × 20K requests | $1,336/mo | $82.56/mo |
| Document extraction 20K in / 1.5K out × 50K requests | $2,655/mo | $178.55/mo |
| Bulk classification 500 in / 20 out × 5M requests | $5,050/mo | $355.00/mo |
Which should you pick?
You are running this at volume
Qwen3.8 Flash
Lowest measured cost per point of capability at $0.2009 per point — the gap compounds with every request.
Quality matters more than the bill
GPT-6.1 Sol
Highest overall LiveBench score of the two at 85.4.
The workload is coding or agentic work
GPT-6.1 Sol
Leads on coding — 96.0 against 53.5.
GPT-6.1 Sol vs Qwen3.8 Flash FAQ
Which is better, GPT-6.1 Sol or Qwen3.8 Flash?
GPT-6.1 Sol scores higher, Qwen3.8 Flash costs less — it depends on your workload. GPT-6.1 Sol is ahead by 38.5 points overall, and Qwen3.8 Flash lists 17× cheaper per blended million tokens. Whether 38.5 points is worth that depends on how much a wrong answer costs you. Qwen3.8 Flash also leads on measured cost per point of capability, at $0.2009 per point.
Is GPT-6.1 Sol cheaper than Qwen3.8 Flash?
Qwen3.8 Flash is cheaper. On a 3:1 input:output blend, GPT-6.1 Sol lists at $4.00 per million tokens and Qwen3.8 Flash at $0.230 — Qwen3.8 Flash is 17× cheaper. Input and output are priced separately — GPT-6.1 Sol charges $2.00 in and $10.00 out, Qwen3.8 Flash charges $0.150 and $0.470 — so the model that looks cheaper flips depending on how output-heavy your workload is.
GPT-6.1 Sol vs Qwen3.8 Flash: which scores higher on benchmarks?
GPT-6.1 Sol scores 85.4 and Qwen3.8 Flash scores 47.0 overall on LiveBench, the mean of its seven categories. That is a 38.5-point lead for GPT-6.1 Sol. 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-6.1 Sol or Qwen3.8 Flash?
Qwen3.8 Flash. 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-6.1 Sol works out at $0.4971 per point and Qwen3.8 Flash at $0.2009.
Does GPT-6.1 Sol or Qwen3.8 Flash have a bigger context window?
They are effectively the same — 1.1M for GPT-6.1 Sol and 1M for Qwen3.8 Flash.
Do GPT-6.1 Sol and Qwen3.8 Flash support prompt caching?
Both publish a cached-input rate: $0.100 per million for GPT-6.1 Sol and $0.016 for Qwen3.8 Flash, against full input rates of $2.00 and $0.150. 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 2026-09-02, 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.