Mistral: Mixtral 8x7B Instruct
Mistral AI • text->text
Context Window33K
Knowledge CutoffDec 10, 2023
Mistral: Mixtral 8x7B Instruct vs Llama 3.1 70B Instruct
Mixtral 8x7B for sparse MoE efficiency, Llama 3.1 70B for newer architecture and broader fine-tuning community.
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Llama 3.1 70B Instruct
Meta • text->text
Context Window131K
Knowledge CutoffJul 23, 2024
Editorial Verdict
Llama 3.1 70B is the higher-capability successor; Mixtral 8x7B is for MoE-specific use cases.
Llama 3.1 70B significantly outperforms Mixtral 8x7B on reasoning, coding, and instruction-following benchmarks. Mixtral 8x7B was a breakthrough model when released but Llama 3.1 70B's updated training makes it the better choice for most use cases. Mixtral 8x7B is still useful for specific sparse mixture-of-experts use cases.
Mistral: Mixtral 8x7B Instruct
- Significantly higher benchmark performance across reasoning and coding
- Updated training with better instruction following and alignment
- Meta's ecosystem with community fine-tuning resources
Best for: Most use cases requiring strong open-weight model performance
Llama 3.1 70B Instruct
- Sparse mixture-of-experts architecture — efficient for specific token routing
- Still competitive for simpler tasks at efficient compute
- Good for specialized deployments where MoE architecture provides efficiency
Best for: Specialized deployments leveraging sparse MoE architecture efficiency
Pricing sourced from OpenRouter — updates as their catalog changes.
Pricing Comparison
All values pull from OpenRouter and update as their catalog changes.
| Metric | Mistral: Mixtral 8x7B Instruct | Llama 3.1 70B Instruct | Winner |
|---|---|---|---|
| Input (per 1M tokens) | $0.54 | $0.40 | 🏆 Llama 3.1 70B Instruct |
| Output (per 1M tokens) | $0.54 | $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: Mixtral 8x7B Instruct Total
$27Llama 3.1 70B Instruct Total
$20Estimated Monthly Savings: $7 with Llama 3.1 70B Instruct
Capability Signals
Scores are directional estimates from model metadata — not official benchmark results.
Mistral: Mixtral 8x7B InstructMMLU Signal (Reasoning)Llama 3.1 70B Instruct
64%
69%
Mistral: Mixtral 8x7B InstructHumanEval Signal (Coding)Llama 3.1 70B Instruct
71%
76%
Mistral: Mixtral 8x7B InstructAgentic Tooling SignalLlama 3.1 70B Instruct
79%
84%
Mistral: Mixtral 8x7B InstructMultimodal SignalLlama 3.1 70B Instruct
64%
69%
Capabilities Matrix
Feature highlights for architecture and production fit.
Mistral: Mixtral 8x7B Instruct Strengths
- Strong tool-calling and structured response support.
- 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/mixtral-8x7b-instruct",
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: Mixtral 8x7B 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.
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.
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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