LLM·Dex
Newsletter

DeepSeek-R1 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-R1 specs · Mixtral 8×22B specs
Verdict by category
  • PriceDeepSeek-R1

    DeepSeek-R1 publishes pricing ($2.19 / 1M output tokens) while Mixtral 8×22B does not.

  • Context windowDeepSeek-R1

    DeepSeek-R1 accepts 128K tokens vs 64K, 2.0× the room for long documents and codebases.

  • BenchmarksTie

    No directly comparable public benchmarks are available for both models, check the spec sheets for individual scores.

  • ModalitiesTie

    Both handle text.

  • OpennessTie

    Both ship open weights, self-host either one.

On balance DeepSeek-R1 edges ahead, winning 2 of 5 categories against Mixtral 8×22B's 0. DeepSeek-R1 publishes pricing ($2.19 / 1M output tokens) while Mixtral 8×22B does not. DeepSeek-R1 accepts 128K tokens vs 64K, 2.0× the room for long documents and codebases.

No directly comparable public benchmarks are available for both models, check the spec sheets for individual scores. 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-R1 is the newer of the two, released 10 months after Mixtral 8×22B, which usually means a more recent knowledge cutoff and updated safety post-training. DeepSeek-R1 is usually picked for reasoning and math 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

SpecDeepSeek-R1Mixtral 8×22B
ProviderDeepSeekMistral
ReleasedJan 2025Apr 2024
Modalitiestexttext
Context window128K tokens64K tokens
Max output,,
Input · 1M$0.55 / 1M tokensPricing not published
Output · 1M$2.19 / 1M tokensPricing not published
Knowledge cutoff2024-07,
Open weightsYes (MIT)Yes (Apache-2.0)
API availableYesYes

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$4.94 vs ,
  • Heavy usage, 1M in / 500k out per day$49.35 vs ,
  • RAG workload, 5M in / 200k out per day$95.64 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).

Price calculator

Estimated spend for the listed models at your usage. Numbers are derived from each model's published per-million-token rates.

  • DeepSeek-R1$0.165
  • 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.

DeepSeek-R1Mixtral 8×22B
  • MMLU,77.8
  • GPQA71.5
Pick DeepSeek-R1 if

DeepSeek-R1 fits when…

  • Open-weight reasoning model on par with o1
  • MIT license
  • Cheap reasoning per token
  • Long-context tasks, handles 128K tokens vs 64K for Mixtral 8×22B.
Pick Mixtral 8×22B if

Mixtral 8×22B fits when…

  • Apache-2.0
  • MoE economics
  • Mature
Don't want either?

Consider DeepSeek-V3

DeepSeek's flagship 671B-parameter MoE, frontier-level quality at a tiny fraction of frontier prices.

See DeepSeek-V3 specsDeepSeek-R1 vs DeepSeek-V3Mixtral 8×22B vs DeepSeek-V3

Frequently asked

  • Is DeepSeek-R1 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-R1 accepts 128K tokens vs 64K for Mixtral 8×22B.
  • Is DeepSeek-R1 or Mixtral 8×22B better for coding?
    Both DeepSeek-R1 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-R1 is licensed under MIT; Mixtral 8×22B under Apache-2.0.
  • When were DeepSeek-R1 and Mixtral 8×22B released?
    DeepSeek-R1 was released by DeepSeek on 2025-01-20. Mixtral 8×22B was released by Mistral on 2024-04-10.
Friday digest

The week's AI launches, in your inbox.

One short email every Friday, new models, leaks, and quietly-shipped APIs you missed.