Llama 3 70B Instruct vs Mixtral 8x7B
Llama 3 70B Instruct (2024) and Mixtral 8x7B (2023) are compact production models from AI at Meta and MistralAI. Llama 3 70B Instruct ships a 8k-token context window, while Mixtral 8x7B ships a 32k-token context window. On HumanEval, Mixtral 8x7B leads by 7.9 pts. On pricing, Mixtral 8x7B costs $0.15/1M input tokens versus $0.40/1M for the alternative. This comparison covers specs, pricing, API access, capabilities, benchmarks, input and output token costs, and production fit for coding and agent workloads.
Mixtral 8x7B is ~167% cheaper at $0.15/1M; pay for Llama 3 70B Instruct only for provider fit.
Decision scorecard
Local evidence first| Signal | Llama 3 70B Instruct | Mixtral 8x7B |
|---|---|---|
| Best for | provider-routed production | provider-routed production |
| Decision fit | Coding, Classification, and JSON / Tool use | Coding and Classification |
| Context window | 8k | 32k |
| Cheapest output | $0.40/1M tokens | $0.45/1M tokens |
| Provider routes | 18 tracked | 18 tracked |
| Shared benchmarks | 2 rows | HumanEval leader |
Decision tradeoffs
- Llama 3 70B Instruct holds a shared-benchmark lead on Massive Multitask Language Understanding, ahead by 1.8 points.
- Llama 3 70B Instruct has the lower cheapest tracked output price at $0.40/1M tokens.
- Llama 3 70B Instruct uniquely exposes Structured outputs in local model data.
- Local decision data tags Llama 3 70B Instruct for Coding, Classification, and JSON / Tool use.
- Mixtral 8x7B holds a shared-benchmark lead on HumanEval, ahead by 7.9 points.
- Mixtral 8x7B has the larger context window for long prompts, retrieval packs, or transcript analysis.
- Local decision data tags Mixtral 8x7B for Coding and Classification.
Monthly cost at traffic
Estimate token spend from the cheapest tracked input and output route or tier on this page.
Llama 3 70B Instruct
$420
Cheapest tracked route/tier: Hyperbolic AI Inference
Mixtral 8x7B
$233
Cheapest tracked route/tier: Mistral AI Studio
Estimated monthly gap: $188. Batch, cache, alternate speed tiers, and negotiated pricing are excluded from this local estimate.
Switch friction
- Provider overlap exists on Databricks Foundation Model Serving, NVIDIA NIM, and GCP Vertex AI; start route-level A/B tests there.
- Mixtral 8x7B is $0.05/1M tokens higher on cheapest tracked output pricing, so quality gains need to justify the spend.
- Check replacement coverage for Structured outputs before moving production traffic.
- Provider overlap exists on GCP Vertex AI, AWS Bedrock, and Microsoft Foundry; start route-level A/B tests there.
- Llama 3 70B Instruct is $0.05/1M tokens lower on cheapest tracked output pricing before cache, batch, or negotiated discounts.
- Llama 3 70B Instruct adds Structured outputs in local capability data.
Specs
| Specification | ||
|---|---|---|
| Released | 2024-04-18 | 2023-12-11 |
| Context window | 8k | 32k |
| Parameters | 70B | 8x7B |
| Architecture | decoder only | mixture of experts |
| License | Llama 3 Community | Apache 2.0(OSI) |
| Openness | Open weights | Open source |
| Commercial use | Commercial use with conditions | Commercial use allowed |
| Knowledge cutoff | 2023-12 | 2023-12 |
Pricing and availability
| Pricing attribute | Llama 3 70B Instruct | Mixtral 8x7B |
|---|---|---|
| Input price | $0.40/1M tokens | $0.15/1M tokens |
| Output price | $0.40/1M tokens | $0.45/1M tokens |
| Providers |
Capabilities
| Capability | Llama 3 70B Instruct | Mixtral 8x7B |
|---|---|---|
| Vision | No | No |
| Multimodal | No | No |
| Reasoning | No | No |
| Function calling | No | No |
| Tool use | No | No |
| Structured outputs | Yes | No |
| Code execution | No | No |
| IDE integration | No | No |
| Computer use | No | No |
| Parallel agents | No | No |
Benchmarks
| Benchmark | Llama 3 70B Instruct | Mixtral 8x7B |
|---|---|---|
| HumanEval | 72.6 | 80.5 |
| Massive Multitask Language Understanding | 82.0 | 80.2 |
Deep dive
On shared benchmark coverage, HumanEval has Llama 3 70B Instruct at 72.6 and Mixtral 8x7B at 80.5, with Mixtral 8x7B ahead by 7.9 points; Massive Multitask Language Understanding has Llama 3 70B Instruct at 82 and Mixtral 8x7B at 80.2, with Llama 3 70B Instruct ahead by 1.8 points. The largest visible gap is 7.9 points on HumanEval, which matters most when that benchmark mirrors your workload. Treat isolated benchmark wins as directional, because provider routing, prompt style, and tool access can move real application results.
The capability footprint differs most on structured outputs: Llama 3 70B Instruct. Both models share the core language-model surface, so the practical split is not just feature count. Use those differences to decide whether the page is about raw model quality, agentic coding support, multimodal ingestion, or predictable structured API behavior.
For cost, Llama 3 70B Instruct lists $0.40/1M input and $0.40/1M output tokens on the cheapest tracked provider, while Mixtral 8x7B lists $0.15/1M input and $0.45/1M output tokens on the cheapest tracked provider. A 70/30 input-output blend puts Mixtral 8x7B lower by about $0.16 per million blended tokens. Availability is 18 providers versus 18, so concentration risk also matters.
Choose Llama 3 70B Instruct when provider fit are central to the workload. Choose Mixtral 8x7B when long-context analysis, larger context windows, and lower input-token cost are more important. For production, rerun your own prompts through the exact provider, region, and tool stack you plan to ship.
FAQ
Which has a larger context window, Llama 3 70B Instruct or Mixtral 8x7B?
Mixtral 8x7B supports 32k tokens, while Llama 3 70B Instruct supports 8k tokens. That gap matters most for long documents, large codebases, retrieval-heavy agents, and conversations where earlier context must remain visible.
Which is cheaper, Llama 3 70B Instruct or Mixtral 8x7B?
Mixtral 8x7B is cheaper on tracked token pricing. Llama 3 70B Instruct costs $0.40/1M input and $0.40/1M output tokens. Mixtral 8x7B costs $0.15/1M input and $0.45/1M output tokens. Provider discounts or batch pricing can still change the final bill.
Is Llama 3 70B Instruct or Mixtral 8x7B open source?
Llama 3 70B Instruct is listed under Llama 3 Community. Mixtral 8x7B is listed under Apache 2.0. License labels affect whether you can self-host, redistribute weights, or rely only on hosted APIs, so confirm the upstream license before deployment.
Which is better for structured outputs, Llama 3 70B Instruct or Mixtral 8x7B?
Llama 3 70B Instruct has the clearer documented structured outputs signal in this comparison. If structured outputs is mission-critical, validate it against the provider endpoint because model-level support and API-level exposure can differ.
Where can I run Llama 3 70B Instruct and Mixtral 8x7B?
Llama 3 70B Instruct is available on GCP Vertex AI, AWS Bedrock, Microsoft Foundry, NVIDIA NIM, and DeepInfra. Mixtral 8x7B is available on Databricks Foundation Model Serving, NVIDIA NIM, GCP Vertex AI, AWS Bedrock, and OctoAI API (Deprecated). Provider coverage can affect latency, region availability, compliance posture, and fallback options.
When should I pick Llama 3 70B Instruct over Mixtral 8x7B?
Mixtral 8x7B is ~167% cheaper at $0.15/1M; pay for Llama 3 70B Instruct only for provider fit. If your workload also depends on provider fit, start with Llama 3 70B Instruct; if it depends on long-context analysis, run the same evaluation with Mixtral 8x7B.
Continue comparing
Last reviewed: 2026-05-22. Data sourced from public model cards and provider documentation.