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Grok-3 vs Mixtral 8x7B

Grok-3 (2026) and Mixtral 8x7B (2023) are frontier reasoning models from xAI and MistralAI. Grok-3 ships a 1M-token context window, while Mixtral 8x7B ships a 32K-token context window. On pricing, Mixtral 8x7B costs $0.15/1M input tokens versus $0.8/1M 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.

Mixtral 8x7B is ~433% cheaper at $0.15/1M; pay for Grok-3 only for reasoning depth.

Specs

Released2026-01-152023-12-11
Context window1M32K
Parameters1B8x7B
Architecture-mixture of experts
LicenseProprietaryApache 2.0
Knowledge cutoff2025-042023-12

Pricing and availability

Grok-3Mixtral 8x7B
Input price$0.8/1M tokens$0.15/1M tokens
Output price$2.4/1M tokens$0.45/1M tokens
Providers

Capabilities

Grok-3Mixtral 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 vision: Grok-3, multimodal input: Grok-3, reasoning mode: Grok-3, function calling: Grok-3, tool use: Grok-3, and structured outputs: Grok-3. 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-3 lists $0.8/1M input and $2.4/1M 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 $1.04 per million blended tokens. Availability is 3 providers versus 18, so concentration risk also matters.

Choose Grok-3 when reasoning depth 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. It also helps separate model capability from provider packaging, which can change cost and latency.

FAQ

Which has a larger context window, Grok-3 or Mixtral 8x7B?

Grok-3 supports 1M 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. Use this as a quick comparison signal, then confirm the provider-specific limits before committing to production.

Which is cheaper, Grok-3 or Mixtral 8x7B?

Mixtral 8x7B is cheaper on tracked token pricing. Grok-3 costs $0.8/1M input and $2.4/1M 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-3 or Mixtral 8x7B open source?

Grok-3 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 vision, Grok-3 or Mixtral 8x7B?

Grok-3 has the clearer documented vision signal in this comparison. If vision is mission-critical, validate it against the provider endpoint because model-level support and API-level exposure can differ. Use this as a quick comparison signal, then confirm the provider-specific limits before committing to production.

Which is better for multimodal input, Grok-3 or Mixtral 8x7B?

Grok-3 has the clearer documented multimodal input signal in this comparison. If multimodal input is mission-critical, validate it against the provider endpoint because model-level support and API-level exposure can differ.

Where can I run Grok-3 and Mixtral 8x7B?

Grok-3 is available on OpenRouter, xAI Console, and Chutes AI. 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.

Continue comparing

Last reviewed: 2026-04-24. Data sourced from public model cards and provider documentation.