DeepSeek V4.1 Flash vs Gemini 3.8 Flash vs Qwen3.8 Flash

The practical choice is workload-dependent: DeepSeek V4.1 Flash leads on direct published USD token cost, Gemini 3.8 Flash has the broadest native multimodal and built-in tool surface, and Qwen3.8 Flash combines 1M context with regional deployment options and familiar API protocols. This is a documented-capability comparison, not a benchmark winner claim.

Checked September 11, 2026 against the vendors’ official documentation. Prices can change and are not directly comparable across currencies, regions and promotional windows. For purchase, pricing math and first-request instructions, use the consolidated DeepSeek V4.1 Flash API guide.

The short comparison

Context size is only a capacity limit. It does not prove that a model retrieves the right evidence, follows a tool contract or produces a usable 100K-token answer. Test those behaviors separately.

Pricing: compare a fixed workload, not three headline numbers

For 1 million uncached input tokens plus 200,000 output tokens, the vendors’ listed rates produce these examples:

DeepSeek also lists cache-hit input at $0.003/M off-peak and $0.006/M peak. Gemini lists cached input at $0.075/M during its introductory period, plus cache storage. Qwen’s Beijing listing shows ¥0.10/M for cache hits. The Qwen total stays in CNY deliberately: converting it without fixing an exchange rate and billing region would create false precision.

Price per token is not price per completed task. Include retries, output length, cache hit rate, tool failures and any grounding charges. Gemini’s standard rates are scheduled to change after the introductory period; Qwen prices differ by region.

Multimodality and tools create the clearest product split

Choose Gemini first when a single request must natively combine audio, video, PDFs and images, or when Google Search grounding and code execution are central to the workflow. Its native API exposes more managed tools than the other two in this comparison.

Choose Qwen first when image/video understanding, a 1M context window and Alibaba Cloud regional deployment are all requirements. Its OpenAI- and Anthropic-compatible protocols can also reduce client migration work, though feature parity still needs testing.

Choose DeepSeek first when the workload is primarily text, screenshots or coding-agent traffic and direct token cost is a major constraint. It supports image input, tool calls and familiar client protocols, with a much larger published maximum output than Gemini or Qwen. A high maximum does not mean every answer should be long.

API compatibility is not behavioral compatibility

An OpenAI-compatible endpoint usually lets you reuse authentication patterns, chat payloads and SDK plumbing. It does not guarantee identical tool schemas, streaming events, reasoning controls, multimodal encoding or error responses. Native features often require the vendor’s own API.

For each candidate, run the same harness:

1. Use 20–50 representative tasks, including failures and edge cases.
2. Pin region, model ID, temperature, tool definitions and output schema.
3. Record accepted result rate, time to first useful output, total tokens and retries.
4. Test rate-limit recovery and one provider outage scenario.
5. Calculate cost per accepted result, not cost per request.

Which one should you shortlist?

There is no responsible universal winner from the specification sheets. Use the table to select two finalists, then let your own acceptance test decide.

Official sources

• DeepSeek model and pricing documentation
• Gemini 3.8 Flash model documentation
• Gemini API pricing
• Qwen3.8 Flash documentation