Answer in brief
DeepSeek’s new Flash combines vision with an architecture change. Against dated Qwen and Mistral alternatives, deployment size and license terms matter as much as benchmark claims.
A September release changes what old aliases call
DeepSeek’s 10 September changelog gives open model deployment a concrete new decision: V4.1 Flash adds native visual understanding and replaces earlier Flash offerings. The current API documentation says retired V4 Flash and Flash Vision experimental names now route to V4.1 Flash. A familiar request name can therefore reach a different model than an older experiment used.
That is a reason to record the underlying version when comparing results. We checked the current documentation on 1 October and contrast this release with downloadable Qwen3.6-27B and Mistral Medium 3.5. Those are verified April and May releases, respectively. They are practical dated alternatives, rather than alleged new October launches or an exhaustive ranking of open models.
Architecture matters more than the Flash label
The official DeepSeek card describes a 552B backbone and an additional 196B Engram memory component. It activates 8B parameters per token during prefill and 16B during decoding. Those are computation figures, not the complete weight-storage footprint. The architecture table identifies the components instead of presenting the active count as the total model size.
Qwen’s 27B dense model and Mistral’s 128B dense model provide different deployment starting points. Their published context capacities are separate from working memory needed for a chosen batch and precision. Our editorial inference is to budget weights, context cache and serving overhead independently before deciding which hardware or hosted endpoint is practical.
| Model | Architecture / parameters | Documented context tokens | Weight license |
|---|---|---|---|
| DeepSeek-V4.1-Flash | MoE: 552B + Engram 196B | 1,000,000 | MIT |
| Qwen3.6-27B | Dense: 27B | 262,144 | Apache 2.0 |
| Mistral Medium 3.5 | Dense: 128B | 256K | Modified MIT; revenue exceptions |
Benchmark evidence needs the exact agent setup
DeepSeek reports its instruct benchmark results at effort 100, temperature 1 and top_p 0.95. Terminal-Bench 4.0 uses DeepSeek Harness Minimal; DeepSWE v1.1 uses mini-SWE to match that benchmark’s requirements. The table preserves those distinctions and marks rival cells Unknown because equivalent Qwen and Mistral runs were not verified in these sources.
A score from another version of Terminal-Bench or a different software-engineering suite cannot fill that gap. Neither can an active-parameter advantage. For a coding team, the useful evidence is a patch that passes its checks under the intended runtime, tool parser and context budget. These published figures help define a trial; VJOURNAL did not execute it.
| Measure | DeepSeek-V4.1-Flash | Qwen3.6-27B | Mistral Medium 3.5 | Evaluation setup |
|---|---|---|---|---|
| Terminal-Bench 4.0 (%) | 31.2% | Unknown | Unknown | DeepSeek Harness Minimal |
| DeepSWE v1.1 (%) | 74.2% | Unknown | Unknown | mini-SWE |
Downloadable weights come with different terms
The official cards identify MIT for DeepSeek, Apache 2.0 for Qwen and a modified MIT license for Mistral with revenue-related exceptions. These are material differences in the deployment choice. The actual license attached to the selected weights should govern that review, rather than a general claim that all open models have interchangeable conditions.
The hosted API and a downloaded checkpoint are also different operating arrangements. With local weights, a team manages its runtime, updates and capacity; a hosted service adds its own access and service terms. Keeping the checkpoint and its configuration recorded makes a later comparison meaningful when an API alias or a serving implementation changes.
The useful alternative is a complete working stack
A realistic comparison starts with a repository, representative images and written acceptance criteria. Hold the tools constant, name the checkpoint, record reasoning and quantization settings, and inspect structured outputs as well as final prose. Count review effort and unsuccessful attempts. A capacity specification cannot substitute for that finished-work evidence.
At the October cutoff, the three documented options offer different balances of architecture, control and license conditions. The decision is whether a particular stack finishes the task within the available infrastructure. Hardware requirements for the reader’s workload and a shared three-model benchmark remain Unknown here, so no composite performance or cost winner is declared.
Questions and answers
Are all three models downloadable?
The official repositories publish weights for the named models. The license and runtime requirements differ, so downloadable does not mean identical deployment conditions.
Is DeepSeek Flash a small local model?
Flash has sparsely activated computation, but the card describes a 552B backbone plus 196B Engram memory. Active parameters per token do not equal the weights that must be stored.
Why are rival benchmark cells Unknown?
We did not verify Qwen or Mistral results under the exact DeepSeek setups shown. Unknown represents a gap in comparable evidence, rather than a zero score for those models.