DeepSeek V4 Pro 0813 vs Mistral Medium 3.5Which Is Better in 2026?
DeepSeek V4 Pro 0813 vs Mistral Medium 3.5: which should you choose in 2026?
DeepSeek V4 Pro 0813 (by DeepSeek) and Mistral Medium 3.5 (by Mistral AI) are compared below. Here is how they stack up on benchmarks, price, and capabilities, and which one to pick in 2026.
There are not enough shared, protocol-compatible benchmark results to declare a performance leader.
DeepSeek V4 Pro 0813 is about 5.5× cheaper on a blended 3:1 input/output basis ($0.54375 vs $3.00 per 1M tokens).
DeepSeek V4 Pro 0813 has the larger context window (1,048,576 tokens vs 262,144 tokens).
Choose DeepSeek V4 Pro 0813 if…
- • lower blended API cost matters for your workload
- • you work with longer documents, transcripts, or codebases
Choose Mistral Medium 3.5 if…
- • you need image inputs
The benchmark count includes only results measured with a matching benchmark version and protocol. Arena scores are shown separately. Missing, preliminary, and incompatible data is not treated as a controlled win. Published point-score comparisons are labeled separately when protocol details are incomplete. For text-output models, the verdict also compares token pricing and context windows.
Performance benchmarks
Every value links to its source. A dash means that no reviewed result is available for that exact model and protocol.
| Benchmark | DeepSeek V4 Pro 0813 | Mistral Medium 3.5 |
|---|---|---|
A 657-task benchmark of multi-step work across simulated SaaS applications in six business domains. The Artificial Analysis protocol reports a guardrail-aware score and is distinct from both the public Zapier split and the unrelated dynamic AutoBench framework. Guardrail-aware score | — | — |
A benchmark of difficult, verifiable information-seeking questions designed to measure an agent's ability to locate hard-to-find facts through web browsing. Accuracy | — | — |
A verified computer-use benchmark in which multimodal agents operate desktop applications and are graded from the resulting environment state. Mean task reward | — | — |
A human-validated subset of real GitHub issues used to measure whether a coding agent can produce repository patches that resolve the associated tests. Resolved | — | — |
Version 2.1 of the benchmark for completing realistic tasks in terminal environments. Harness, resource limits, and attempt count are part of the protocol. Mean task success | — | — |
A dynamic LLM evaluation framework in which models generate questions, answer them, and participate in reciprocal peer assessment. AutoBench is distinct from Zapier's AutomationBench. Weighted peer-assessment score | — | — |
The highest-quality subset of Graduate-Level Google-Proof Q&A, designed to test expert-level scientific reasoning in biology, physics, and chemistry. Accuracy | — | — |
A 2,500-question expert-level benchmark spanning dozens of academic fields. Tool-assisted and no-tools results are separate protocols and must not be merged. Accuracy | — | — |
| Arena preference scores | ||
Human preference score | — | 1427 ±7 |
Human preference score for code and web development | — | 1266 ±15 |
Benchmark sources
Reviewed evidence last updated 2026-08-09. Arena data last refreshed Aug 12, 2026.
Pricing, capabilities, and model facts
| Feature | DeepSeek V4 Pro 0813 | Mistral Medium 3.5 |
|---|---|---|
| Context & model facts | ||
| Developer | DeepSeek | Mistral AI |
| API provider | DeepSeek | Mistral |
| Input context | 1,048,576 tokens | 262,144 tokens |
| Maximum output | 384,000 tokens | — |
| Released | — | — |
| Added to Writingmate | Aug 12, 2026 | Apr 30, 2026 |
| License | Not available | Not available |
| Knowledge cutoff | — | — |
| Capabilities | ||
| Inputs | Text | Text, Image, File |
| Outputs | Text | Text |
| Provider endpoint accepts tool parameters | Yes | Yes |
| Reasoning | Yes | Yes |
| Vision | No | Yes |
| Image Generation | No | No |
| Video Generation | No | No |
| API pricing | ||
| Input (per 1M tokens) | $0.435 | $1.50 |
| Output (per 1M tokens) | $0.87 | $7.50 |
| Blended 3:1 input/output | $0.54375 | $3.00 |
| API performance | ||
| p95 latency | Not measured | Not measured |
| Output throughput | Not measured | Not measured |
| Writingmate shows API performance only when both models have enough observations from the same measurement window, prompt profile, and provider. Third-party latency values are not copied into this table. | ||
Data sources
- Writingmate model catalog (pricing, limits, and availability)
Catalog data last updated Aug 12, 2026.
Why Pay for Multiple Subscriptions?
Comparing DeepSeek V4 Pro 0813 from DeepSeek with Mistral Medium 3.5 from Mistral AI? Instead of managing separate API keys and subscriptions, get both with Writingmate.
| Plan | Price | DeepSeek V4 Pro 0813 | Mistral Medium 3.5 | AI Images | AI Video |
|---|---|---|---|---|---|
Writingmate Pro Most popular | $20/mo | Included | Included | Nano Banana Pro, FLUX.2, DALL-E & more | Sora 2, VEO 3.1 |
Writingmate Ultimate Power users | $60/mo | Included | Included | Nano Banana Pro, FLUX.2, DALL-E & more | Sora 2, VEO 3.1 |
DeepSeek V4 Pro 0813 vs Mistral Medium 3.5 FAQ
Which is better, DeepSeek V4 Pro 0813 or Mistral Medium 3.5?
There are not enough shared, protocol-compatible benchmark results to declare a performance leader. DeepSeek V4 Pro 0813 is about 5.5× cheaper on a blended 3:1 input/output basis ($0.54375 vs $3.00 per 1M tokens). DeepSeek V4 Pro 0813 has the larger context window (1,048,576 tokens vs 262,144 tokens).
Which model is cheaper to use through an API?
DeepSeek V4 Pro 0813 is about 5.5× cheaper on a blended 3:1 input/output basis ($0.54375 vs $3.00 per 1M tokens).
Which model supports more context?
DeepSeek V4 Pro 0813 has the larger context window (1,048,576 tokens vs 262,144 tokens).
Can I switch between DeepSeek V4 Pro 0813 and Mistral Medium 3.5?
Yes. Use the model selector on this page to open any current Writingmate model comparison. You can also run the same prompt with both models in Writingmate.
Related comparisons
More DeepSeek V4 Pro 0813 comparisons
Seed 2.1 Turbo
ByteDance
Seed 2.1 Turbo is a multimodal model from ByteDance Seed for coding and long-horizon agent workflows.
Qwen3.8 2.4T A95B
Qwen
Qwen3.8 2.4T A95B is an open-weight sparse mixture-of-experts model from Qwen and the open-weight variant of Qwen3.8 Max, with 95 billion active parameters out of 2.4 trillion total.
Seed-2.0-Code
ByteDance
Seed 2.0 Code is a model from ByteDance Seed optimized for agentic coding.
Grok 4.6
xAI
Grok 4.6 is SpaceXAI's smartest model with frontier performance on coding, knowledge work, and STEM.
Nemotron 3.5 Lightning
NVIDIA
NVIDIA Nemotron 3.5 Lightning is an open mixture-of-experts model from NVIDIA, with 3B active parameters out of 30B total.
Sakana Namazu
Sakana AI
Sakana Namazu is a Japanese-specialized reasoning model from Sakana AI, based on Kimi K2.6 with additional training for Japanese language and business contexts.
More Mistral Medium 3.5 comparisons
Grok 4.6
xAI
Grok 4.6 is SpaceXAI's smartest model with frontier performance on coding, knowledge work, and STEM.
Sakana Namazu
Sakana AI
Sakana Namazu is a Japanese-specialized reasoning model from Sakana AI, based on Kimi K2.6 with additional training for Japanese language and business contexts.
Muse Spark 1.2
Meta
Muse Spark 1.2 is a reasoning model from Meta, designed for complex agentic tasks.
Claude Opus 5
Anthropic
Claude Opus 5 is Anthropic’s flagship model for demanding reasoning, coding, and long-horizon agentic work.
Gemini 3.6 Flash
Gemini 3.6 Flash is a high-efficiency model from Google for coding, agentic workflows, and web and app development.
Gemini 3.5 Flash Lite
Gemini 3.5 Flash Lite is a high-efficiency model from Google with upgraded agentic capabilities.