Mixtral 8×22B for open-source llms
Mixtral 8×22B is ranked #6 on LLMDex's open-source llms ranking out of 8 models we track for this use case. Below, the specific reasons it slots where it does, and when you should reach for an alternative.
Updated
At a glance
- Rank
- #6 of 8
- Context
- 64K tokens
- Output / 1M
- Pricing not published
- Released
- Apr 2024
Why Mixtral 8×22B fits this task
Three things about Mixtral 8×22B that map directly onto what this task rewards: Apache-2.0; MoE economics. Beyond the task-specific fit, Mixtral 8×22B also brings mature, both of which compound when the workload broadens.
The criteria this task rewards
LLMDex ranks best open-source llms on 5 criteria , these are the axes the ranking uses, in priority order:
- Composite benchmark performance
- License permissiveness (Apache, MIT, custom OSS)
- Inference economics on commodity GPUs
- Fine-tuning ecosystem maturity
- Multilingual coverage
How Mixtral 8×22B scores on each axis
Where Mixtral 8×22B costs you: older generation. For most teams this is acceptable on this workload, the value of the strengths above outweighs the cost. For cost-bound workloads or teams with strict latency budgets, run an eval against the next two ranked models on real data before committing.
Strengths that pay off here
- Apache-2.0
- MoE economics
- Mature
Tracked weaknesses
- Older generation
- 64k context
When to pick something else
If you can pay slightly more or accept slightly different tradeoffs, DeepSeek-R1 from DeepSeek ranks one position higher and tends to win on the hardest cases. First open-weight reasoning model to match o1, the release that proved RL-from-scratch reasoning training was reproducible.
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Other models for open-source llms
- Llama 4 405B for open-source llms
Meta's flagship open-weight model, sparse MoE design competitive with closed-frontier flagships.
Read guide - DeepSeek-V3 for open-source llms
DeepSeek's flagship 671B-parameter MoE, frontier-level quality at a tiny fraction of frontier prices.
Read guide - Qwen3-72B for open-source llms
Alibaba's flagship open-weight Qwen3, strong on multilingual, code, and math, Apache-2.0 licensed.
Read guide - Llama 4 70B for open-source llms
Meta's mid-tier Llama 4, the practical workhorse for self-hosted deployments.
Read guide - DeepSeek-R1 for open-source llms
First open-weight reasoning model to match o1, the release that proved RL-from-scratch reasoning training was reproducible.
Read guide
Mixtral 8×22B for other use cases
Direct comparisons
Frequently asked
Is Mixtral 8×22B good for open-source llms?
Mixtral 8×22B is ranked #6 on LLMDex's open-source llms list. Mistral's largest open-weight MoE, Apache-2.0, still widely deployed.How much does Mixtral 8×22B cost for open-source llms?
Mistral has not published per-token pricing for Mixtral 8×22B at the time of writing.What's a cheaper alternative to Mixtral 8×22B for open-source llms?
The next ranked model on this task is Qwen2.5-72B. Compare both before committing.When should I NOT use Mixtral 8×22B for open-source llms?
Tracked weakness: Older generation. If that constraint is binding for your workload, the next-ranked model on this task is the safer pick.
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