Llama 3.1 70B Instruct
Meta • Text
Context Window128K
Knowledge CutoffUnknown
Llama 3.1 70B Instruct vs Mistral Large
Mistral Large for hosted premium quality, Llama 3.1 70B for self-hosted open-weight flexibility.
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Mistral Large
Mistral AI • Text
Context Window128K
Knowledge CutoffUnknown
Editorial Verdict
Both are outperformed by DeepSeek V3 on benchmarks. Choose Llama for ecosystem, Mistral for GDPR.
DeepSeek V3 outperforms Llama 3.1 70B and Mistral Large on most benchmarks. Between Llama 3.1 70B and Mistral Large, Llama has broader English performance and community support; Mistral Large offers European data residency. For capability, both lose to DeepSeek V3. For ecosystem and deployment preferences, choose between Llama (English/ecosystem) and Mistral (European/GDPR).
Llama 3.1 70B Instruct
- Meta's ecosystem with extensive fine-tuning resources and community support
- Strong English language performance across diverse tasks
- Well-integrated with most inference engines and deployment frameworks
Best for: English-centric deployments with ecosystem and community resource needs
Mistral Large
- European data residency for GDPR compliance
- Fast inference optimized for European infrastructure
- Good multilingual performance for European language applications
Best for: GDPR-sensitive European deployments requiring data residency
Pricing sourced from OpenRouter — updates as their catalog changes.
Pricing Comparison
All values pull from OpenRouter and update as their catalog changes.
| Metric | Llama 3.1 70B Instruct | Mistral Large | Winner |
|---|---|---|---|
| Input (per 1M tokens) | Custom | Custom | N/A |
| Output (per 1M tokens) | Custom | Custom | N/A |
| 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.
Llama 3.1 70B Instruct Total
VariableMistral Large Total
VariableSavings unavailable (pricing not published for one model).
Capability Signals
Scores are directional estimates from model metadata — not official benchmark results.
Llama 3.1 70B InstructMMLU Signal (Reasoning)Mistral Large
69%
69%
Llama 3.1 70B InstructHumanEval Signal (Coding)Mistral Large
69%
69%
Llama 3.1 70B InstructAgentic Tooling SignalMistral Large
69%
69%
Llama 3.1 70B InstructMultimodal SignalMistral Large
69%
69%
Capabilities Matrix
Feature highlights for architecture and production fit.
Llama 3.1 70B Instruct Strengths
- Balanced general-purpose profile for chat, extraction, and automation tasks.
Mistral Large Strengths
- Balanced general-purpose profile for chat, extraction, and automation tasks.
API Implementation
Quick start snippets for each provider style.
Meta (Python)
from openai import OpenAI
client = OpenAI()
response = client.chat.completions.create(
model="meta/llama-3.1-70b-instruct",
messages=[{"role": "user", "content": "Hello"}]
)
print(response.choices[0].message.content)Mistral AI (Python)
from openai import OpenAI
client = OpenAI()
response = client.chat.completions.create(
model="mistral-ai/mistral-large",
messages=[{"role": "user", "content": "Hello"}]
)
print(response.choices[0].message.content)Choose Llama 3.1 70B Instruct when...
- You want a balanced default for mixed chat and workflow automation workloads.
- You can measure quality with your own benchmark and prompt set.
Choose Mistral Large when...
- 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.
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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