Grok Code Fast 1 vs Mixtral 8x7B
Grok Code Fast 1 (2025) and Mixtral 8x7B (2023) are agentic coding models from xAI and MistralAI. Grok Code Fast 1 ships a 262K-token context window, while Mixtral 8x7B ships a 32K-token context window. On pricing, Mixtral 8x7B costs $0.15/1M input tokens versus $0.2/1M for the alternative. This comparison covers specs, pricing, capabilities, benchmarks, provider availability, and production fit. It focuses on practical selection signals rather than broad model-family marketing.
Grok Code Fast 1 fits 8x more tokens; pick it for long-context work and Mixtral 8x7B for tighter calls.
Specs
| Released | 2025-08-27 | 2023-12-11 |
| Context window | 262K | 32K |
| Parameters | 314B | 8x7B |
| Architecture | mixture of experts | mixture of experts |
| License | Proprietary | Apache 2.0 |
| Knowledge cutoff | - | 2023-12 |
Pricing and availability
| Grok Code Fast 1 | Mixtral 8x7B | |
|---|---|---|
| Input price | $0.2/1M tokens | $0.15/1M tokens |
| Output price | $1.5/1M tokens | $0.45/1M tokens |
| Providers |
Capabilities
| Grok Code Fast 1 | Mixtral 8x7B | |
|---|---|---|
| Vision | ||
| Multimodal | ||
| Reasoning | ||
| Function calling | ||
| Tool use | ||
| Structured outputs | ||
| Code execution |
Benchmarks
No shared benchmark rows are currently sourced for this pair.
Deep dive
The capability footprint differs most on function calling: Grok Code Fast 1, tool use: Grok Code Fast 1, and structured outputs: Grok Code Fast 1. 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, Grok Code Fast 1 lists $0.2/1M input and $1.5/1M output tokens, 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 $0.35 per million blended tokens. Availability is 1 providers versus 18, so concentration risk also matters.
Choose Grok Code Fast 1 when coding workflow support and larger context windows are central to the workload. Choose Mixtral 8x7B when provider fit, lower input-token cost, and broader provider choice are more important. For production, rerun your own prompts through the exact provider, region, and tool stack you plan to ship. This keeps the decision grounded in measurable tradeoffs instead of brand-level assumptions.
FAQ
Which has a larger context window, Grok Code Fast 1 or Mixtral 8x7B?
Grok Code Fast 1 supports 262K tokens, while Mixtral 8x7B supports 32K tokens. That gap matters most for long documents, large codebases, retrieval-heavy agents, and conversations where earlier context must remain visible.
Which is cheaper, Grok Code Fast 1 or Mixtral 8x7B?
Mixtral 8x7B is cheaper on tracked token pricing. Grok Code Fast 1 costs $0.2/1M input and $1.5/1M 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 Grok Code Fast 1 or Mixtral 8x7B open source?
Grok Code Fast 1 is listed under Proprietary. 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 function calling, Grok Code Fast 1 or Mixtral 8x7B?
Grok Code Fast 1 has the clearer documented function calling signal in this comparison. If function calling is mission-critical, validate it against the provider endpoint because model-level support and API-level exposure can differ.
Which is better for tool use, Grok Code Fast 1 or Mixtral 8x7B?
Grok Code Fast 1 has the clearer documented tool use signal in this comparison. If tool use is mission-critical, validate it against the provider endpoint because model-level support and API-level exposure can differ.
Where can I run Grok Code Fast 1 and Mixtral 8x7B?
Grok Code Fast 1 is available on OpenRouter. Mixtral 8x7B is available on Databricks Foundation Model Serving, NVIDIA NIM, GCP Vertex AI, AWS Bedrock, and OctoAI API. Provider coverage can affect latency, region availability, compliance posture, and fallback options.
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Last reviewed: 2026-04-24. Data sourced from public model cards and provider documentation.