Mistral Large
Mistral AI • text+file->text
Context Window128K
Knowledge CutoffFeb 26, 2024
Mistral Large vs Llama 3.1 70B Instruct
Mistral Large for hosted premium quality with EU data residency, Llama 3.1 70B for self-hosted ecosystem.
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Llama 3.1 70B Instruct
Meta • text->text
Context Window131K
Knowledge CutoffJul 23, 2024
Editorial Verdict
Llama 3.1 70B wins on capability; Mistral Large wins on GDPR compliance.
Llama 3.1 70B outperforms Mistral Large on most benchmarks while Mistral Large offers European data residency for GDPR compliance. For maximum capability, Llama 3.1 70B wins. For GDPR-sensitive European deployments, Mistral Large is the better choice despite slightly lower benchmarks.
Mistral Large
- Higher benchmark performance on reasoning, coding, and instruction following
- Meta's ecosystem with extensive fine-tuning resources and community
- Better integrated with global inference engines and frameworks
Best for: Maximum capability with broad ecosystem support
Llama 3.1 70B Instruct
- European data residency for GDPR compliance
- Fast inference optimized for European infrastructure
- Good multilingual performance for European language applications
Best for: GDPR-sensitive European enterprise deployments
Pricing sourced from OpenRouter — updates as their catalog changes.
Pricing Comparison
All values pull from OpenRouter and update as their catalog changes.
| Metric | Mistral Large | Llama 3.1 70B Instruct | Winner |
|---|---|---|---|
| Input (per 1M tokens) | $2.00 | $0.40 | 🏆 Llama 3.1 70B Instruct |
| Output (per 1M tokens) | $6.00 | $0.40 | 🏆 Llama 3.1 70B Instruct |
| Request fee | N/A | N/A | N/A |
| Image fee | N/A | N/A | N/A |
Cost Estimator
Estimate monthly billing using real OpenRouter prices.
Mistral Large Total
$160Llama 3.1 70B Instruct Total
$20Estimated Monthly Savings: $140 with Llama 3.1 70B Instruct
Capability Signals
Scores are directional estimates from model metadata — not official benchmark results.
Mistral LargeMMLU Signal (Reasoning)Llama 3.1 70B Instruct
76%
69%
Mistral LargeHumanEval Signal (Coding)Llama 3.1 70B Instruct
86%
76%
Mistral LargeAgentic Tooling SignalLlama 3.1 70B Instruct
84%
84%
Mistral LargeMultimodal SignalLlama 3.1 70B Instruct
69%
69%
Capabilities Matrix
Feature highlights for architecture and production fit.
Mistral Large Strengths
- Strong tool-calling and structured response support.
- High coding signal for production assistant use cases.
- Balanced general-purpose profile for chat, extraction, and automation tasks.
Llama 3.1 70B Instruct Strengths
- Strong tool-calling and structured response support.
- Balanced general-purpose profile for chat, extraction, and automation tasks.
API Implementation
Quick start snippets for each provider style.
Mistral AI (Python)
from openai import OpenAI
client = OpenAI()
response = client.chat.completions.create(
model="mistralai/mistral-large",
messages=[{"role": "user", "content": "Hello"}]
)
print(response.choices[0].message.content)Meta (Python)
from openai import OpenAI
client = OpenAI()
response = client.chat.completions.create(
model="meta-llama/llama-3.1-70b-instruct",
messages=[{"role": "user", "content": "Hello"}]
)
print(response.choices[0].message.content)Choose Mistral Large when...
- You rely on function calling, tool chaining, and structured output contracts.
- You want a balanced default for mixed chat and workflow automation workloads.
- You can measure quality with your own benchmark and prompt set.
Choose Llama 3.1 70B Instruct when...
- You rely on function calling, tool chaining, and structured output contracts.
- You want a balanced default for mixed chat and workflow automation workloads.
- You can measure quality with your own benchmark and prompt set.
Frequently Asked Questions
Practical checks before selecting a production model.
Is Mistral Large better than Llama 3.1 70B?
It depends on the workload. Mistral Large and Llama 3.1 70B differ in pricing, context window, and capability mix — see the side-by-side table above for the breakdown.
What's the price difference between Mistral Large and Llama 3.1 70B?
Mistral Large: $2.00 per 1M input tokens. Llama 3.1 70B: $0.40 per 1M input tokens. Output token pricing typically runs 2-5x higher.
Which model is better for coding?
Coding preference depends on your stack and tool-calling needs. Compare the coding signal row, test with your repository tasks, and validate latency in your target region.
Which model is cheaper at scale?
Input and output token pricing can diverge by workload profile. Use the estimator with your monthly request count and token mix to get a realistic cost difference.
Are these official benchmark numbers?
Pricing is live from OpenRouter. Benchmark rows are ModelsAtlas metadata-based signals and should be treated as directional guidance, not official leaderboard scores.
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