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DeepSeek-V3 vs Qwen2.5-72B

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 · Qwen2.5-72B specs
Verdict by category
  • PriceDeepSeek-V3

    DeepSeek-V3 publishes pricing ($1.10 / 1M output tokens) while Qwen2.5-72B does not.

  • Context windowTie

    Both ship a 128K-token context window.

  • 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 86.0.

  • ModalitiesTie

    Both handle text.

  • OpennessTie

    Both ship open weights, self-host either one.

On balance DeepSeek-V3 edges ahead, winning 2 of 5 categories against Qwen2.5-72B's 0. DeepSeek-V3 publishes pricing ($1.10 / 1M output tokens) while Qwen2.5-72B does not. Both ship a 128K-token context window.

DeepSeek-V3 leads in 1 of 1 shared benchmarks; the biggest gap is on MMLU (broad academic knowledge), where it scores 88.5 vs 86.0. 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 3 months after Qwen2.5-72B, 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 Qwen2.5-72B 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-V3Qwen2.5-72B
ProviderDeepSeekAlibaba
ReleasedDec 2024Sep 2024
Modalitiestexttext
Context window128K tokens128K tokens
Max output,,
Input · 1M$0.27 / 1M tokensPricing not published
Output · 1M$1.10 / 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$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).

Price calculator

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
  • Qwen2.5-72BPricing 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-V3Qwen2.5-72B
  • MMLU88.586.0
  • HumanEval90.0
Pick DeepSeek-V3 if

DeepSeek-V3 fits when…

  • Frontier-level quality at open-weight prices
  • MIT license, clean commercial use
  • Cheap to serve via MoE architecture
Pick Qwen2.5-72B if

Qwen2.5-72B fits when…

  • Mature deployment
  • Apache-2.0
  • Strong multilingual
Don't want either?

Consider DeepSeek-R1

First open-weight reasoning model to match o1, the release that proved RL-from-scratch reasoning training was reproducible.

See DeepSeek-R1 specsDeepSeek-V3 vs DeepSeek-R1Qwen2.5-72B vs DeepSeek-R1

Frequently asked

  • Is DeepSeek-V3 or Qwen2.5-72B 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?
    Both DeepSeek-V3 and Qwen2.5-72B ship a 128K-token context window.
  • Is DeepSeek-V3 or Qwen2.5-72B better for coding?
    Both DeepSeek-V3 and Qwen2.5-72B 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; Qwen2.5-72B under Apache-2.0.
  • When were DeepSeek-V3 and Qwen2.5-72B released?
    DeepSeek-V3 was released by DeepSeek on 2024-12-26. Qwen2.5-72B was released by Alibaba on 2024-09-19.
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