Mistral AI (24 models) vs DeepSeek (13 models) - compared across composite scores, pricing, capabilities, and context windows.
| Capability | Mistral AI | DeepSeek | Leader |
|---|---|---|---|
Vision | 11/24 | 0/13 | Mistral AI |
Reasoning | 2/24 | 11/13 | DeepSeek |
Function Calling | 21/24 | 10/13 | Mistral AI |
JSON Mode | 22/24 | 12/13 | Mistral AI |
Web Search | 0/24 | 0/13 | Tie |
Streaming | 24/24 | 13/13 | Mistral AI |
Image Output | 0/24 | 0/13 | Tie |
| Metric | Mistral AI | DeepSeek |
|---|---|---|
| Cheapest Input (per 1M tokens) | $0.020 Mistral Nemo | $0.140 DeepSeek V4 Flash |
| Cheapest Output (per 1M tokens) | $0.030 | $0.280 |
| Most Expensive Input (per 1M tokens) | $2.00 Mistral Medium 3.5 | $0.700 R1 |
| Most Expensive Output (per 1M tokens) | $7.50 | $2.50 |
| Free Models | 0 | 0 |
| Max Context Window | 262K | 1.0M |
| Model | Score | Input $/M | Output $/M |
|---|---|---|---|
| Mistral Large 3 2512 | 67 | $0.500 | $1.50 |
| Mistral Large | 66 | $2.00 | $6.00 |
| Mixtral 8x22B Instruct | 63 | $2.00 | $6.00 |
| Mistral Large 2407 | 56 | $2.00 | $6.00 |
| Devstral Small 1.1 | 47 | $0.100 | $0.300 |
| Devstral 2 2512 | 46 | $0.400 | $2.00 |
| Devstral Medium | 45 | $0.400 | $2.00 |
| Mistral Medium 3.5 | 40 | $1.50 | $7.50 |
| Mistral Small 4 | 40 | $0.150 | $0.600 |
| Ministral 3 14B 2512 | 40 | $0.200 | $0.200 |
| Ministral 3 8B 2512 | 40 | $0.150 | $0.150 |
| Ministral 3 3B 2512 | 40 | $0.100 | $0.100 |
| Mistral Medium 3.1 | 40 | $0.400 | $2.00 |
| Codestral 2508 | 40 | $0.300 | $0.900 |
| Mistral Small 3.2 24B | 40 | $0.075 | $0.200 |
| Mistral Small 3.1 24B | 40 | $0.350 | $0.560 |
| Saba | 40 | $0.200 | $0.600 |
| Mistral Small 3 | 40 | $0.050 | $0.080 |
| Mistral Large 2411 | 40 | $2.00 | $6.00 |
| Pixtral Large 2411 | 40 | $2.00 | $6.00 |
| Model | Score | Input $/M | Output $/M |
|---|---|---|---|
| R1 0528 | 79 | $0.500 | $2.15 |
| DeepSeek V4 Pro | 76 | $0.435 | $0.870 |
| R1 | 73 | $0.700 | $2.50 |
| DeepSeek V4 Flash | 72 | $0.140 | $0.280 |
| DeepSeek V3 0324 | 72 | $0.200 | $0.770 |
| DeepSeek V3.2 | 70 | $0.252 | $0.378 |
| DeepSeek V3.2 Exp | 70 | $0.270 | $0.410 |
| DeepSeek V3 | 70 | $0.320 | $0.890 |
| DeepSeek V3.1 Terminus | 69 | $0.270 | $0.950 |
| DeepSeek V3.1 | 69 | $0.150 | $0.750 |
| R1 Distill Llama 70B | 42 | $0.700 | $0.800 |
| DeepSeek V3.2 Speciale | 40 | $0.287 | $0.431 |
| R1 Distill Qwen 32B | 37 | $0.290 | $0.290 |
Compare any two AI providers side-by-side.
Mistral AI's $0.040/M token floor (vs DeepSeek's $0.290/M) reflects a portfolio strategy where open source models drive adoption while commercial variants monetize enterprise usage. With 14 of 25 models open source, Mistral can undercut DeepSeek's pricing by 86% at the low end while their top model (Mistral Small 4 at 51/100) still outperforms DeepSeek's best (V3.2 Exp at 46/100) by 5 points.
DeepSeek achieves 91% reasoning coverage across its compact 11-model portfolio while Mistral only reaches 40% vision coverage despite having 25 models total. This specialization tradeoff means DeepSeek excels at complex reasoning tasks but completely lacks vision capabilities (0/11), while Mistral offers broader multimodal options at the cost of minimal reasoning support (1/25 models).
Mistral's 88% function calling coverage versus DeepSeek's 73% reflects different target markets: Mistral optimizes for API integration and tool use while DeepSeek focuses on raw reasoning performance. Despite Mistral's function calling advantage, both providers average 40-42/100 overall, suggesting DeepSeek compensates with stronger core language capabilities in its reasoning-focused models.
Mistral's 60% larger maximum context window (262K vs 164K) comes at a premium, with their high-end models reaching $6.00/M tokens compared to DeepSeek's $2.50/M ceiling. For applications processing large documents, Mistral's extra 98K tokens of context may justify the 2.4x price increase, but DeepSeek's 164K window handles most use cases at 58% lower cost.
Both providers' zero web search coverage creates opportunities for competitors with real-time data access, but their different strengths suggest distinct workarounds. Mistral's 22/25 function calling models can integrate external search APIs, while DeepSeek's 10/11 reasoning models excel at synthesizing pre-loaded knowledge bases without real-time lookups.