DeepSeek V4-Pro wins on coding (93 vs 91) and price ($0.435 vs $1.5/1M input) and context window (1M vs 256K). For most workflows, DeepSeek V4-Pro is the stronger default — best open-weights flagship — near-frontier coding at a tenth of the price.
The safest Mistral Medium 3.5 vs DeepSeek V4-Pro default, the cheaper option worth trying first, and the specialist pick — before you read the detail below.
DeepSeek V4-Pro is the strongest answer here for Mistral Medium 3.5 vs DeepSeek V4-Pro — pick it when quality of output matters more than the $0.43/1M/1M input you pay for it.
GPT-5.1-Codex-Max is the cheaper way in for Mistral Medium 3.5 vs DeepSeek V4-Pro, at $1.25/1M/1M input against DeepSeek V4-Pro's $0.43/1M/1M.
Mistral Medium 3.5 is the fastest of these for Mistral Medium 3.5 vs DeepSeek V4-Pro — worth it when latency is what the reader notices, not the last few points of reasoning depth.
DeepSeek V4-Pro leads on coding with a score of 93 vs 91 for Mistral Medium 3.5.
DeepSeek V4-Pro has the larger context window: 1M vs 256K for Mistral Medium 3.5.
DeepSeek V4-Pro is cheaper at $0.435/1M input tokens vs $1.5/1M for Mistral Medium 3.5.
Go with DeepSeek V4-Pro if you want one model to handle coding and reasoning — it targets frontier-level coding and reasoning on a budget.
Switch to Mistral Medium 3.5 when your work is mostly self-hostable European multimodal coding; on that narrower brief it is the better tool.
Both models serve different primary workflows — DeepSeek V4-Pro for frontier-level coding and reasoning on a budget, Mistral Medium 3.5 for self-hostable European multimodal coding — so running each where it has a clear edge often beats forcing one to do both.
Switch the scoring lens to see whether the Mistral Medium 3.5 vs DeepSeek V4-Pro answer changes when cost, speed, or long-document depth leads the decision.
DeepSeek / Budget / Aug 6, 2026
Best open-weights flagship — near-frontier coding at a tenth of the price.
Ranks models by the broadest mix of coding, writing, research, and long-context usefulness.
You need vision input, verified agentic performance, or predictable pricing (surge pricing and an announced increase loom).
Where the Mistral Medium 3.5 vs DeepSeek V4-Pro recommendation shifts once you weigh price or latency differently.
Best self-hostable multimodal model — European, dense, MIT-licensed.
Best open-weights flagship — near-frontier coding at a tenth of the price.
List prices and published scores — the numbers this page's pick is built from.
| Model | Input | Output | Est. month | Context | Speed | Coding | Writing | Research |
|---|---|---|---|---|---|---|---|---|
| DeepSeek V4-ProDeepSeek | $0.43/1M | $0.87/1M | $6.09 | 1M tokens | Balanced | 93 | 80 | 85 |
| Mistral Medium 3.5Mistral | $1.50/1M | $7.50/1M | $30 | 256k tokens | Balanced | 91 | 84 | 82 |
Scores out of 100 — how we evaluate models. “Est. month” is 10M in / 2M out at list price: a ceiling, no discounts.
Why each one is on the shortlist for Mistral Medium 3.5 vs DeepSeek V4-Pro, what it is genuinely good at, and where we would steer you away from it.
The default answer for Mistral Medium 3.5 vs DeepSeek V4-Pro — 93/100 on the coding axis, and the model we would start with unless the price below rules it out.
DeepSeek's 1.6T-parameter (49B active) MoE flagship with hybrid sparse attention — near-frontier coding and reasoning at roughly a tenth of closed-rival pricing, MIT-licensed open weights.
You need vision input, verified agentic performance, or predictable pricing (surge pricing and an announced increase loom).
The open-weights frontier flagship of 2026. Self-reported numbers flatter it and independent agentic scores land lower, but even discounted it's the most capability per dollar in the directory's upper tier — with MIT-licensed weights.
Full pricing, benchmark table and release notes on the DeepSeek V4-Pro page.
The fastest model in this shortlist for Mistral Medium 3.5 vs DeepSeek V4-Pro. Pick it when turnaround is what your readers or users notice.
A 128B dense open-weight multimodal model handling reasoning, coding, and vision in one set of weights — frontier-adjacent coding at mid-tier prices, self-hostable under a modified MIT license.
API price-performance is all that matters — DeepSeek V4-Pro is stronger and cheaper hosted.
The best open-weights model you can realistically self-host. DeepSeek V4 beats it on benchmarks and price via API, but at 128B dense with vision, Medium 3.5 is what you can actually run on your own hardware with EU data residency.
Full pricing, benchmark table and release notes on the Mistral Medium 3.5 page.
UseRightAI recommendations are based on practical decision factors people actually feel in day-to-day use.
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DeepSeek V4-Pro wins on more of the categories we score — coding, reasoning, budget — so it is the better default of the two. Mistral Medium 3.5 is the better pick when your work is mostly self-hostable European multimodal coding. Neither is universally "better": DeepSeek V4-Pro is aimed at frontier-level coding and reasoning on a budget, Mistral Medium 3.5 at self-hostable European multimodal coding.
DeepSeek V4-Pro is cheaper at $0.435/1M input and $0.87/1M output. Mistral Medium 3.5 costs $1.5/1M input and $7.5/1M output.
DeepSeek V4-Pro has the larger context window at 1M tokens vs Mistral Medium 3.5's 256K. For large document analysis, DeepSeek V4-Pro is the stronger pick.
DeepSeek V4-Pro is better for coding with a score of 93 vs Mistral Medium 3.5's 91 (out of 100). GPT-6 Astra is the overall coding leader in this directory at 100/100.
Both Mistral Medium 3.5 and DeepSeek V4-Pro have similar speed profiles — rated balanced. Neither will be the bottleneck if latency is your deciding factor.
Independent harnesses report much lower agentic scores than the self-reported numbers; trails GPT-5.6 and Opus-class on hard agentic evals. Peak-hour surge pricing doubles rates, a price increase is announced, and it's text-only (no vision). Avoid it if you need vision input, verified agentic performance, or predictable pricing (surge pricing and an announced increase loom). That is the main case for looking at Mistral Medium 3.5 instead.
Trails GPT-5.6, Opus-class, and Gemini frontier models on complex multi-step reasoning; sparse published benchmark disclosure. 256K context is a quarter of the 1M frontier norm, and $7.50/1M output is dear for the tier. Avoid it if API price-performance is all that matters — DeepSeek V4-Pro is stronger and cheaper hosted. Against DeepSeek V4-Pro specifically, the gap shows up most on coding (93 vs 91).
Take a moderate workload of 10M input and 2M output tokens a month. Mistral Medium 3.5 runs $30.00 (at $1.5/1M in and $7.5/1M out); DeepSeek V4-Pro runs $6.09 (at $0.435/1M in and $0.87/1M out). That is a $23.91/month difference — DeepSeek V4-Pro is the cheaper of the two at this volume, and the gap scales linearly as you send more. Output tokens dominate the bill on both, so prompt length matters far less than response length.
Yes, and for most teams that beats picking one. A common split is DeepSeek V4-Pro for frontier-level coding and reasoning on a budget, with Mistral Medium 3.5 handling self-hostable European multimodal coding. Since DeepSeek V4-Pro is both the stronger and the cheaper option here, a split mainly makes sense if Mistral Medium 3.5 covers a capability you specifically need.