CorX Labs

DeepSeek-V3

by DeepSeek · China
Open weightsTool callingMixture of experts131K context

Trained for a reported $5.6M in GPU time — the release that reset assumptions about what frontier training has to cost.


Specification

The numbers

Maker
DeepSeek
Released
2024-12
Parameters
671B total / 37B active
Architecture
MoE
Context window
131,072 tokens
Max output
8,192 tokens
Input
Text
Output
Text
Reasoning
No
Tool calling
Yes
Knowledge cutoff
Not reported
Licence
MIT
Availability
Not reported
Weights
Downloadable

Cost

Price per million tokens

Input
$0.27 / M tokens
Output
$1.10 / M tokens
Blended 3:1
$0.478

This model has open weights, so there is no first-party price. The figures above are a representative third-party hosting rate — you can also run it yourself for the cost of the hardware.

Published scores

Benchmarks

Figures published by DeepSeek or taken from a public leaderboard. Row last checked 2026-08.

DeepSeek-V3 by capability category, with its rank among models reporting the same tests.
CategoryScore Rank
ReasoningGPQA Diamond59.1%59 of 83 reporting the same tests
MathsCompetition mathematics, graded on the final answerNot reported
CodingHumanEval82.6%24 of 46 reporting the same tests
KnowledgeMMLU-Pro75.9%12 of 77 reporting the same tests
MultimodalReading charts, diagrams and photographsNot reported
Instruction followingObeying an exact, checkable formatNot reported
Human preferenceLMArena Elo131810 of 24 reporting the same tests

A category averages every benchmark in it that DeepSeek-V3 reports. The rank counts only models that report the same tests, so it never compares an average over three benchmarks against an average over one.

Every reported test

MMLU-ProKnowledge75.9%Rank 12 of 77 models reporting
GPQA DiamondReasoning59.1%Rank 59 of 83 models reporting
AIME 2025MathsNot reportedNo figure published
MATH-500MathsNot reportedNo figure published
SWE-bench VerifiedCodingNot reportedNo figure published
SWE-bench ProCodingNot reportedNo figure published
Terminal-Bench 2.1CodingNot reportedNo figure published
Frontier-Bench v0.1ReasoningNot reportedNo figure published
Terminal-Bench 4.0CodingNot reportedNo figure published
LiveCodeBenchCodingNot reportedNo figure published
HumanEvalCoding82.6%Rank 24 of 46 models reporting
MMMUMultimodalNot reportedNo figure published
IFEvalInstruction followingNot reportedNo figure published
LMArena EloHuman preference1318Rank 10 of 24 models reporting

Where these numbers come from

Every score on this page is a published figure, taken from the model's own card, system card, technical report or release post, or from a public leaderboard. CorX Labs did not run these evaluations. Most are self-reported by the lab that built the model, which means they were produced under that lab's own choice of prompt, scaffold and number of attempts — so treat them as a starting point for a shortlist, not as a settled ranking.

A score someone other than the model's maker measured is marked Independent and names its measurer. Those are the stronger numbers on this page — an outside harness has no reason to flatter anyone — and there are not many of them.

Where a figure has not been published, the cell reads Not reported rather than an estimate. Nothing here is inferred, interpolated or guessed. Each model records the month its row was last checked. Full method and caveats.

Head to head

DeepSeek-V3 compared

Same maker

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