Meta (Llama) (12 models) vs Mistral AI (19 models) - compared across composite scores, pricing, capabilities, and context windows.
| Capability | Meta (Llama) | Mistral AI | Leader |
|---|---|---|---|
Vision | 4/12 | 10/19 | Mistral AI |
Reasoning | 0/12 | 2/19 | Mistral AI |
Function Calling | 6/12 | 17/19 | Mistral AI |
JSON Mode | 8/12 | 18/19 | Mistral AI |
Web Search | 0/12 | 0/19 | Tie |
Streaming | 12/12 | 19/19 | Mistral AI |
Image Output | 0/12 | 0/19 | Tie |
| Metric | Meta (Llama) | Mistral AI |
|---|---|---|
| Cheapest Input (per 1M tokens) | $0.020 Llama 3.1 8B Instruct | $0.020 Mistral Nemo |
| Cheapest Output (per 1M tokens) | $0.030 | $0.030 |
| Most Expensive Input (per 1M tokens) | $0.400 Llama 4 Maverick | $2.00 Mistral Medium 3.5 |
| Most Expensive Output (per 1M tokens) | $0.600 | $7.50 |
| Free Models | 2 | 0 |
| Max Context Window | 10.0M | 262K |
| Model | Score | Input $/M | Output $/M |
|---|---|---|---|
| Llama 4 Maverick | 67 | $0.150 | $0.600 |
| Llama 3.3 70B Instruct | 66 | $0.100 | $0.320 |
| Llama 3.3 70B Instruct (free) | 66 | Free | Free |
| Llama 3.1 70B Instruct | 65 | $0.400 | $0.400 |
| Llama 4 Scout | 55 | $0.100 | $0.300 |
| Llama 3.1 8B Instruct | 44 | $0.020 | $0.030 |
| Llama Guard 4 12B | 40 | $0.180 | $0.180 |
| Llama 3.2 11B Vision Instruct | 40 | $0.345 | $0.345 |
| Llama 3 8B Instruct | 34 | $0.140 | $0.140 |
| Llama 3.2 3B Instruct (free) | 33 | Free | Free |
| Llama 3.2 3B Instruct | 33 | $0.051 | $0.335 |
| Llama 3.2 1B Instruct | 18 | $0.027 | $0.201 |
| Model | Score | Input $/M | Output $/M |
|---|---|---|---|
| Mistral Medium 3.5 | 71 | $1.50 | $7.50 |
| 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 2 2512 | 45 | $0.400 | $2.00 |
| 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 |
| Voxtral Small 24B 2507 | 40 | $0.100 | $0.300 |
| 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 | $0.351 | $0.555 |
| Saba | 40 | $0.200 | $0.600 |
| Mistral Small 3 | 40 | $0.050 | $0.080 |
| Mistral Nemo | 40 | $0.020 | $0.030 |
| Mistral Medium 3 | 14 | $0.400 | $2.00 |
Compare any two AI providers side-by-side.
Mistral AI clearly prioritizes API integration capabilities, with 88% of their portfolio supporting function calling compared to Meta's 50%. This reflects Mistral's commercial focus on production deployments, while Meta's open-source strategy emphasizes research flexibility over standardized API features.
Despite having 11 fewer models overall, Meta achieves 28.6% vision coverage versus Mistral's 40%, suggesting Meta is more selective but less comprehensive. Mistral's vision-capable models span a wider price range ($0.24-$6.00 per 1M tokens) compared to Meta's narrower band, offering more deployment flexibility for multimodal applications.
Meta concentrates innovation in flagship models while maintaining a 34/100 average across their 14-model portfolio, versus Mistral's more consistent 40/100 average across 25 models. This 3-point leadership gap at the top tier suggests Meta prioritizes breakthrough performance over portfolio consistency.
Meta's 3.8x larger context window enables document-heavy workflows that Mistral cannot handle, though this comes at a cost - Meta's pricing tops out at $0.740 per 1M tokens versus Mistral's $6.00 maximum. For applications requiring massive context, Meta becomes the only viable choice despite Mistral's stronger average performance (40 vs 34).
Meta commits 100% to open source with 2 completely free models, while Mistral reserves 11 models as proprietary with zero free tier. This philosophical split means Meta users get full transparency and self-hosting options across the entire portfolio, while Mistral users accessing their 11 proprietary models face vendor lock-in but gain exclusive features like their sole reasoning model.