# Mixtral 8x22B API Rate Limits, Pricing & Performance (July 2026) - Tool: AI API Limits & Performance Matrix - Last updated: July 2026 ## TL;DR Mixtral 8x22B is a legacy Mistral AI MoE model superseded by Mistral Small 3.1 and Medium 3.5. Its first-party availability on La Plateforme is unconfirmed as of July 2026. Open weights (Apache 2.0) are freely available for self-hosting and are widely served by third-party hosts including Together AI, Fireworks, and DeepInfra. For new projects, migrate to Mistral Medium 3.5. ## Frequently asked questions ### Does Mixtral 8x22B still have a working API? As of July 2026, Mixtral 8x22B's first-party availability on Mistral's La Plateforme is unconfirmed — it is no longer a promoted SKU on the main pricing page. You can still access it through third-party inference hosts (Together AI, Fireworks, DeepInfra) or by self-hosting the Apache 2.0 open weights. Verify current first-party status on mistral.ai/pricing before building any dependency on the La Plateforme endpoint. ### What is Mixtral 8x22B pricing? The historical La Plateforme rate was $2.00 per million input tokens and $6.00 per million output tokens — treat these as historical reference only since current first-party availability is unconfirmed. Third-party host pricing varies; verify on Together AI, Fireworks, or DeepInfra pricing pages directly. Self-hosting via the Apache 2.0 open weights costs $0 per token (infrastructure only). ### How do I self-host Mixtral 8x22B? Download the open weights from Hugging Face (Apache 2.0 license permits commercial use at no fee). You need approximately 141GB VRAM at FP16 — for example, 8× A100 80GB GPUs — or roughly 70GB VRAM using int4 quantization. The MoE architecture activates only ~39B of 141B total parameters per forward pass, giving reasonable throughput relative to the total parameter count. ### What should I migrate to from Mixtral 8x22B? Mistral Medium 3.5 is the recommended replacement: it has 128K context (vs Mixtral 8x22B's 65K), active development, a confirmed first-party endpoint on La Plateforme with EU data residency, and comparable pricing. For higher quality, Mistral Large 3 is the flagship alternative. Both are actively developed and have confirmed La Plateforme endpoints. RapidDev can help scope a migration plan for your existing Mixtral 8x22B integration — rapidevelopers.com/contact. ### Is Mixtral 8x22B open source? Yes — Mixtral 8x22B is released under the Apache 2.0 license, which permits commercial use, modification, and distribution without a licensing fee. The open weights are available on Hugging Face. This makes it one of the more permissively licensed large-scale MoE models available for self-hosting. ### How much VRAM does Mixtral 8x22B need? Approximately 141GB VRAM at FP16 precision — equivalent to 8× A100 80GB GPUs or similar high-VRAM setup. With int4 quantization, the requirement drops to approximately 70GB VRAM (roughly 2× A100 80GB or 4× A100 40GB), with an acceptable quality tradeoff for most use cases. The MoE architecture activates only ~39B of the 141B total parameters per token, so inference throughput is better than a dense 141B model on the same hardware. ### What is the Mixtral 8x22B context window? Mixtral 8x22B supports a 65K token context window. This is narrower than current actively developed models — Mistral Medium 3.5 and Mistral Large 3 both offer 128K tokens. If your use case requires longer contexts, this is a primary reason to migrate to a current Mistral model. ### How do I fix HTTP 429 errors on Mixtral 8x22B? The fix depends on your access path. For third-party hosts (Together AI, Fireworks, DeepInfra): honor the Retry-After header and use exponential backoff — delay = min(2^attempt, 60) seconds ± 10% jitter. Each host has different rate limits; check their dashboards for your specific ceilings. For La Plateforme (if active): same OpenAI-compatible retry pattern with base_url='https://api.mistral.ai/v1'. For self-hosted deployments, 429 is not applicable — bottleneck will be GPU throughput instead. --- Source: https://www.rapidevelopers.com/ai-api-limits-performance-matrix/mixtral-8x22b © RapidDev — https://www.rapidevelopers.com/ai-api-limits-performance-matrix/mixtral-8x22b