DeepSeek original V3 model, trained on nearly 15 trillion tokens, matches leading closed-source models at a far lower price. Quantized at FP8.
Added Feb 27, 2025
Model weightsContext Window
128.0K
Max Output
8.2K
Avg output tokens (7d)
79 tokens
Input Price (Auto)
$0.10/1M
Output Price (Auto)
$0.42/1M
Cache Read (Auto)
$0.050/1M
Capabilities
Benchmarks
Benchmarks
Performance metrics and benchmarks
Sourced from Artificial Analysis.
Intelligence Index
16.0
Coding Index
21.2
Agentic Index
0.8
Agentic work
AutomationBench-AA
Workflow automation with guardrail penalties
0.3%
Better than 6% of models compared
AA-Briefcase
Agentic knowledge work (Elo)
92 Elo
Better than 7% of models compared
GDPval-AA v2
Economically valuable tasks (Elo)
275 Elo
Better than 12% of models compared
Document reasoning
GDP.pdf
Professional PDF reasoning: all-pass rate
1.6%
Better than 17% of models compared
AA-LCR v1.1
Long context reasoning with updated grading
45.7%
Better than 44% of models compared
Reasoning
HLE
Humanity's Last Exam
11.2%
Better than 58% of models compared
IFBench
Instruction-following benchmark
49.0%
Better than 58% of models compared
Coding
Terminal-Bench v4.0
Practical coding and terminal tasks
0.0%
Better than 17% of models compared
SciCode
Python programming for scientific computing
39.0%
Better than 25% of models compared
LiveCodeBench
Contamination-free coding benchmark
59.3%
Better than 66% of models compared
Math
AIME 2025
American Invitational Mathematics Examination 2025
59.0%
Better than 56% of models compared
AIME
American Invitational Mathematics Examination
52.0%
Better than 72% of models compared
Math-500
Diverse mathematical problem solving benchmark
94.2%
Better than 76% of models compared
Knowledge
MMLU-Pro
Professional and academic subject knowledge
83.7%
Better than 86% of models compared
Legacy benchmarks
GPQA Diamond (legacy)
Graduate-level scientific reasoning
75.1%
Better than 57% of models compared
Terminal-Bench Hard (legacy)
Agentic coding and terminal use
32.6%
Better than 76% of models compared
T²-Bench Telecom (legacy)
Conversational AI agents in dual-control scenarios
78.9%
Better than 69% of models compared
AA-LCR (unversioned / legacy)
Long context reasoning evaluation
45.7%
Better than 44% of models compared
Last updated Sep 13, 2026
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