Alibaba (Qwen) · language model
Qwen3 Coder Flash: pricing, context and benchmarks
Qwen3 Coder Flash is Alibaba (Qwen)'s model released on September 17, 2025. It costs $0.2 per million input tokens and $0.98 per million output tokens, with a 1M-token context window. No independent score has been published for it so far.
What you need to know about Qwen3 Coder Flash
Alibaba (Qwen), based in China, put Qwen3 Coder Flash online on September 17, 2025. It accepts text as input and produces text. Its context window reaches 1,000,000 tokens, larger than 77 % of the models in the registry, and it can write up to 65,536 tokens in one go.
It has been available for about 13 months, long enough for its prices and scores to have settled.
Qwen3 Coder Flash pricing
On OpenRouter, Qwen3 Coder Flash costs $0.2 per million input tokens and $0.98 per million output tokens. Counting three tokens read for every token written, its average price is $0.39 per million, more expensive than 34 % of paid models (2 times cheaper than the median of $0.76).
A concrete example: a task that sends 2,000 tokens (about 1,500 words) and receives a 500-token answer costs $0.0009. At 1,000 tasks a day, budget $26 a month. The price calculator redoes this with your own volumes.
Tokens already sent and cached are billed at $0.039 per million.
Performance and benchmarks
Epoch AI does not yet publish a capabilities index for Qwen3 Coder Flash. We therefore show no made-up performance rating: only price, context and features count towards its score.
Features, privacy and local use
- It supports tool calling, so it can drive an AI agent, call APIs or MCP servers.
- It can answer in an enforced JSON format (structured outputs).
- Its knowledge stops at June 2025.
Its weights are not published: it can only be used through the Alibaba (Qwen) API or a broker such as OpenRouter, which means sending your data off your machines.
Other Alibaba (Qwen) models
| Model | Released | Avg. price | ECI |
|---|---|---|---|
| Qwen3.8 Max Prime | Sep 23, 2026 | $6 | — |
| Qwen3.8 Omni Flash | Sep 21, 2026 | $0.23 | — |
| Qwen3.8 Max (0902) | Sep 3, 2026 | $3 | 155.1 |
| Qwen3.8 Flash | Aug 26, 2026 | $0.23 | — |
| Qwen3.8 27B | Aug 14, 2026 | $0.78 | 151.7 |
| Qwen3.8 2.4T A95B | Aug 12, 2026 | $3 | — |
| Qwen3.7 Flash | Jul 27, 2026 | $0.06 | 144.1 |
| Qwen3.7 Plus | Jun 3, 2026 | $0.56 | 147 |
Verdict
Computed score: 67/100, from public data (performance, price, context, features).
Strengths
- Very large context (1M tokens): long documents or a whole codebase fit in one request.
- Agent-ready: tool calling and structured JSON output.
Limitations
- No independent score published: performance cannot be verified.
- Text only: reads neither images nor files.
Avoid if…
- your data must not leave your servers.
Alternatives
- UnslopNemo 12B$0.4
- MiniMax-01$0.43
- GLM 5.3$0.41
- Llama 4 Maverick$0.3
- Trinity Large Thinking$0.39
- Beam 1.0 501B A23B$0.44
Comparisons and tools
Learn more
Frequently asked questions
How much does Qwen3 Coder Flash cost?
$0.2 per million input tokens and $0.98 per million output tokens on OpenRouter (checked October 25, 2026). A task with 2,000 tokens read and 500 written costs $0.0009.
How big is Qwen3 Coder Flash's context window?
1,000,000 tokens, roughly 750,000 words, with a maximum of 65,536 output tokens.
Can Qwen3 Coder Flash run locally?
No, Alibaba (Qwen) does not publish its weights: it is API-only.
Is Qwen3 Coder Flash suitable for an AI agent?
Technically yes: it supports tool calling and structured outputs. Then check its cost for your volume.
What are the alternatives to Qwen3 Coder Flash?
The closest models on price and performance are UnslopNemo 12B, MiniMax-01, and GLM 5.3.
Related articles
- Mistral AI: models, pricing and agentsOctober 25, 2026
- LLM benchmarks: how to read model rankingsOctober 24, 2026
- Running a local LLM: hardware, software, and limitationsOctober 24, 2026
- Open-source or closed models for your AI agents?October 5, 2026
Neutrality. No affiliate links. No vendor pays to be reviewed, rated or ranked. Reviews are directed by François Chollet, neutral and factual, and every data point is dated and sourced. How we score