DeepSeek-V3 vs Mixtral 8×22B
A complete head-to-head: pricing, context window, benchmarks, modality coverage, and openness, with a programmatic verdict synthesized from the underlying data.
Updated
DeepSeek-V3 specs · Mixtral 8×22B specs- PriceDeepSeek-V3
DeepSeek-V3 publishes pricing ($1.10 / 1M output tokens) while Mixtral 8×22B does not.
- Context windowDeepSeek-V3
DeepSeek-V3 accepts 128K tokens vs 64K, 2.0× the room for long documents and codebases.
- BenchmarksDeepSeek-V3
DeepSeek-V3 leads in 1 of 1 shared benchmarks; the biggest gap is on MMLU (broad academic knowledge), where it scores 88.5 vs 77.8.
- ModalitiesTie
Both handle text.
- OpennessTie
Both ship open weights, self-host either one.
On balance DeepSeek-V3 edges ahead, winning 3 of 5 categories against Mixtral 8×22B's 0. DeepSeek-V3 publishes pricing ($1.10 / 1M output tokens) while Mixtral 8×22B does not. DeepSeek-V3 accepts 128K tokens vs 64K, 2.0× the room for long documents and codebases.
DeepSeek-V3 leads in 1 of 1 shared benchmarks; the biggest gap is on MMLU (broad academic knowledge), where it scores 88.5 vs 77.8. Both target the same set of modalities (text), so the deciding factors are price, context, and raw quality. Both ship open weights, self-host either one.
DeepSeek-V3 is the newer of the two, released 9 months after Mixtral 8×22B, which usually means a more recent knowledge cutoff and updated safety post-training. DeepSeek-V3 is usually picked for open source llm and commercial use llm workloads, while Mixtral 8×22B sees more deployments in open source llm and commercial use llm. If pricing matters more than every last benchmark point, run the numbers in the calculator below before committing.
Side-by-side specs
| Spec | DeepSeek-V3 | Mixtral 8×22B |
|---|---|---|
| Provider | DeepSeek | Mistral |
| Released | Dec 2024 | Apr 2024 |
| Modalities | text | text |
| Context window | 128K tokens | 64K tokens |
| Max output | , | , |
| Input · 1M | $0.27 / 1M tokens | Pricing not published |
| Output · 1M | $1.10 / 1M tokens | Pricing not published |
| Knowledge cutoff | 2024-07 | , |
| Open weights | Yes (MIT) | Yes (Apache-2.0) |
| API available | Yes | Yes |
Pricing at scale
What you'd actually pay at typical workloads. Numbers come from each model's published per-million-token rates.
- Light usage, 100k in / 50k out per day$2.46 vs ,
- Heavy usage, 1M in / 500k out per day$24.60 vs ,
- RAG workload, 5M in / 200k out per day$47.10 vs ,
Light usage, 100k in / 50k out per day: pricing not directly comparable (one or both models are missing public per-token rates). Heavy usage, 1M in / 500k out per day: pricing not directly comparable (one or both models are missing public per-token rates). RAG workload, 5M in / 200k out per day: pricing not directly comparable (one or both models are missing public per-token rates).
Estimated spend for the listed models at your usage. Numbers are derived from each model's published per-million-token rates.
- DeepSeek-V3$0.082
- Mixtral 8×22BPricing unavailable
Benchmarks compared
Only sourced numbers. Where a benchmark is missing for one model we show the available value rather than fabricating the other.
- MMLU88.577.8
- HumanEval90.0
DeepSeek-V3 fits when…
- Frontier-level quality at open-weight prices
- MIT license, clean commercial use
- Cheap to serve via MoE architecture
- Long-context tasks, handles 128K tokens vs 64K for Mixtral 8×22B.
Mixtral 8×22B fits when…
- Apache-2.0
- MoE economics
- Mature
Consider DeepSeek-R1
First open-weight reasoning model to match o1, the release that proved RL-from-scratch reasoning training was reproducible.
Frequently asked
Is DeepSeek-V3 or Mixtral 8×22B cheaper?
Per-token pricing isn't published for at least one of these models, check each model's spec page for current rates.Which has the larger context window?
DeepSeek-V3 accepts 128K tokens vs 64K for Mixtral 8×22B.Is DeepSeek-V3 or Mixtral 8×22B better for coding?
Both DeepSeek-V3 and Mixtral 8×22B are competitive on coding benchmarks. See each model's individual spec page for HumanEval and SWE-bench scores where published. For an opinionated pick, consult our Best LLM for Coding ranking.Are either of these models open source?
Both ship with open weights. DeepSeek-V3 is licensed under MIT; Mixtral 8×22B under Apache-2.0.When were DeepSeek-V3 and Mixtral 8×22B released?
DeepSeek-V3 was released by DeepSeek on 2024-12-26. Mixtral 8×22B was released by Mistral on 2024-04-10.
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