# Mixtral 8x7B API Rate Limits, Pricing & Performance (July 2026)

- Tool: AI API Limits & Performance Matrix
- Last updated: July 2026

## TL;DR

Mixtral 8x7B is a legacy open-weight (Apache 2.0) MoE model with no published first-party La Plateforme price as of July 2026. Access it via Groq (free tier with rate limits), Together AI, Fireworks AI, or DeepInfra at roughly $0.10–0.60/MTok (verify per host). Self-hosting requires ~90GB VRAM FP16 or ~45GB int4. Migrate new projects to Mistral Small 3.1 for 128K context and active support.

## Frequently asked questions

### Does Mixtral 8x7B still have a working API in 2026?

Mixtral 8x7B is a legacy model with no published first-party price on La Plateforme as of July 2026. The most reliable access paths are Groq (free tier available), Together AI, Fireworks AI, and DeepInfra, which all serve the open weights. La Plateforme first-party availability is unconfirmed — verify on mistral.ai/pricing before relying on it.

### Is Mixtral 8x7B free to use?

Yes, in two ways. First, Groq offers a free tier with rate limits for Mixtral 8x7B — check console.groq.com for current RPM and RPD limits. Second, the open weights are Apache 2.0 licensed, meaning you can self-host commercially at zero per-token cost (hardware cost only, requiring ~45GB VRAM at int4). Paid third-party hosting runs roughly $0.10–0.60/MTok blended (verify per host).

### How do I increase Mixtral 8x7B rate limits?

For Groq: upgrade from free to a paid plan at console.groq.com to unlock higher RPM and RPD limits. For other third-party hosts (Together AI, Fireworks AI, DeepInfra): upgrade your plan on the respective host's dashboard. For unlimited throughput, self-host the Apache 2.0 open weights on your own GPU infrastructure (~45GB VRAM at int4, ~90GB at FP16).

### Mixtral 8x7B vs Mistral Medium 3.5 — which should I use?

For new projects, Mistral Medium 3.5 is the recommended choice: it has 128K context (vs Mixtral 8x7B's 32K), an active first-party API on La Plateforme with EU data residency, and ongoing model development. Mixtral 8x7B makes sense only if you have an existing integration you cannot migrate, need Groq's free tier for prototyping, or want to self-host at the smallest possible VRAM footprint.

### What does the Mixtral 8x7B 429 error mean and how do I fix it?

A 429 Too Many Requests error means you have hit your rate limit on the inference host. On Groq, read the x-ratelimit-reset-requests response header to know exactly when your quota resets. On other hosts, honor the Retry-After header. Use exponential backoff: wait = min(2^attempt, 60) seconds plus ±10% jitter. If you consistently hit limits, upgrade your plan or distribute load across multiple hosts.

### How much VRAM does Mixtral 8x7B need to self-host?

Mixtral 8x7B requires approximately 90GB VRAM at FP16 precision (7 or more A100 40GB GPUs) or approximately 45GB VRAM using int4 quantization (3–4 A100 40GB GPUs). The int4 quantized checkpoint on llama.cpp or vLLM is the practical self-hosting path for most teams. The Apache 2.0 license allows commercial self-hosting with no licensing fee.

### What is the Mixtral 8x7B context window limit?

Mixtral 8x7B supports a maximum context window of 32K tokens. This is significantly narrower than current models — Mistral Medium 3.5 supports 128K tokens and Llama 4 Scout supports up to 10M tokens natively. If your application requires longer conversations or larger documents, migrate to a model with a wider context window.

### Can RapidDev help migrate from Mixtral 8x7B to current Mistral models?

Yes. RapidDev's engineering team handles AI gateway and model-migration work including updating model strings, re-benchmarking prompts for context differences, and configuring retry logic across new endpoints. For a free scoping call, visit rapidevelopers.com/contact.

---

Source: https://www.rapidevelopers.com/ai-api-limits-performance-matrix/mixtral-8x7b
© RapidDev — https://www.rapidevelopers.com/ai-api-limits-performance-matrix/mixtral-8x7b