DeepSeek V3.2 vs Mistral Large 3 2512
DeepSeekMistral AIboth via one key, provider price + 3%
Both models shipped the same month (December 2025), but they're built for different jobs. DeepSeek V3.2 is a sparse-attention text model tuned for reasoning and agentic tool use at a low price. Mistral Large 3 2512 is a 675B-parameter (41B active) mixture-of-experts model with multimodal input and a bigger context window, released under Apache 2.0. Both run on OpenKey with one API key and a flat 3% fee on top of provider list price.
Spec vs spec
| Spec | DeepSeek V3.2 | Mistral Large 3 2512 |
|---|---|---|
| Context window | 131K | 262K |
| Max output | 64K | — |
| Input modalities | text | text, image, file |
| Output modalities | text | text |
| Released | Dec 1, 2025 | Dec 1, 2025 |
| Reasoning | optional | — |
Pricing
Per 1M tokens. Provider price plus the flat 3% fee — the sum is what you pay.
deepseek/deepseek-v3.2
Input · 1M tokens
$0.229 + 3%$0.236
Output · 1M tokens
$0.343 + 3%$0.353
Cache read · 1M tokens
$0.023 + 3%$0.024
FEE — FLAT, EVERY MODEL3%
mistralai/mistral-large-2512
Input · 1M tokens
$0.500 + 3%$0.515
Output · 1M tokens
$1.50 + 3%$1.54
Cache read · 1M tokens
$0.050 + 3%$0.052
FEE — FLAT, EVERY MODEL3%
One workload, priced on both
10M input + 2M output tokens at each model's price, flat 3% fee included.
deepseek/deepseek-v3.2Cheaper
$3.06
$2.97 provider + 3%
mistralai/mistral-large-2512
$8.24
$8.00 provider + 3%
Benchmarks
Design Arena categories where both models have results. Higher Elo and lower rank win.
| DeepSeek V3.2 | Mistral Large 3 2512 | |||
|---|---|---|---|---|
| Category | Elo | Rank | Elo | Rank |
| 3D | 1210 | #41 | 1176 | #51 |
| ASCII art | 1129 | #42 | 1115 | #43 |
| Code | 1213 | #48 | 1191 | #56 |
| Data viz | 1203 | #48 | 1180 | #55 |
| Game dev | 1197 | #50 | 1146 | #65 |
| SVG | 1089 | #54 | 1050 | #62 |
| UI components | 1203 | #47 | 1157 | #59 |
| Websites | 1217 | #46 | 1205 | #53 |
Head-to-head preference voting. How we filter and rank
Pricing math
DeepSeek V3.2 costs $0.2288/M input and $0.3432/M output from the provider; on OpenKey that's $0.235664/M input and $0.353496/M output (provider price x 1.03). Mistral Large 3 2512 costs $0.50/M input and $1.50/M output from the provider, or $0.515/M and $1.545/M on OpenKey. Run the same job — 10M input tokens, 2M output tokens — through each: DeepSeek V3.2 comes to $2.97 total, Mistral Large 3 2512 comes to $8.00. That's roughly 2.7x more expensive for Mistral on this workload, driven mostly by output tokens costing 4.4x more per million ($1.545 vs $0.353496).
Design and code benchmarks
Design Arena scores DeepSeek V3.2 ahead of Mistral Large 3 2512 in all eight categories tested: 3d (1210 vs 1176), asciiart (1129 vs 1115), codecategories (1213 vs 1191), dataviz (1203 vs 1180), gamedev (1197 vs 1146), svg (1089 vs 1050), uicomponent (1203 vs 1157), and website (1217 vs 1205). DeepSeek's rank in each category is also better — for example rank 41 vs rank 51 on 3d, and rank 54 vs rank 62 on svg. Mistral Large 3 2512 does have Artificial Analysis scores on record (intelligence index 15.9, coding index 20.1, agentic index 5.5), but DeepSeek has no equivalent Artificial Analysis figures in this data, so that comparison can't be made directly.
Context and modality
Mistral Large 3 2512 supports a 262,144-token context window, double DeepSeek V3.2's 131,072 tokens — useful if you're feeding in long documents or large codebases in one call. Mistral also accepts text, image, and file input; DeepSeek V3.2 is text-only in and out. DeepSeek's tradeoff: a 64,000-token max completion limit, which Mistral's record doesn't specify. If your workload is single-modality text generation with moderate document length, the context gap won't matter much in practice.
Tool use and structured output
DeepSeek V3.2 exposes 19 supported parameters including `reasoning`, `include_reasoning`, `tool_choice`, `tools`, `top_k`, and `logprobs` — more control surface for agentic and reasoning-heavy pipelines. Mistral Large 3 2512 supports 11 parameters, including `tool_choice` and `tools` but no reasoning-mode toggle or logprob access. If you're building agents that need explicit reasoning traces or fine-grained sampling control, DeepSeek's parameter set gives you more to work with.
Which model for which job
| Use case | Pick | Why |
|---|---|---|
| High-volume text generation on a budget | DeepSeek V3.2 | $2.97 vs $8.00 for a 10M-in/2M-out workload |
| Agentic tool-calling pipelines | DeepSeek V3.2 | supports `reasoning` and `include_reasoning` params Mistral lacks |
| Multimodal input (images, files) | Mistral Large 3 2512 | accepts text+image+file input; DeepSeek is text-only |
| Very long document processing | Mistral Large 3 2512 | 262,144-token context vs DeepSeek's 131,072 |
| UI/website/game-dev code generation | DeepSeek V3.2 | higher elo on uicomponent (1203 vs 1157), gamedev (1197 vs 1146), and website (1217 vs 1205) |
| Self-hosting or license-sensitive deployment | Mistral Large 3 2512 | released under Apache 2.0 |
Questions
- Which model is cheaper for a typical workload?
- DeepSeek V3.2, by a wide margin. A 10M-input/2M-output job costs $2.97 on DeepSeek V3.2 versus $8.00 on Mistral Large 3 2512 — about 2.7x more for Mistral, mostly because Mistral's output tokens run $1.545/M on OpenKey versus DeepSeek's $0.353496/M.
- Does DeepSeek V3.2 ever lose to Mistral on Design Arena?
- Not in this data. Across all eight measured categories — 3d, asciiart, codecategories, dataviz, gamedev, svg, uicomponent, website — DeepSeek V3.2's elo score is higher than Mistral Large 3 2512's, including asciiart at 1129 vs 1115.
- Which model handles longer documents?
- Mistral Large 3 2512, with a 262,144-token context window versus DeepSeek V3.2's 131,072 tokens — exactly double. If your input regularly exceeds ~130K tokens, Mistral is the only one of the two that fits without chunking.
- Can either model handle images?
- Only Mistral Large 3 2512. Its input modalities include text, image, and file; DeepSeek V3.2's modality is text-to-text only, with no image or file input support listed.