DeepSeek Chat 0324
DeepSeek V3 0324, DeepSeek's 03 March 2025 V3 model, optimized for general-purpose tasks. Quantized at FP8.
- Tool Calling
- Structured Output
Pricing
Auto routing · per 1M tokens- Input
- $0.20
- Output
- $0.77
- Cache read
- $0.14
Specifications
- Context window
- 128K
- Max output
- 8.2K
- Parameters
- 671B / 37B
- Total / active
- Avg output (7d)
- 486 tokens
- Longer than 42% of models
Benchmarks
Benchmarks
Sourced from Artificial Analysis.
Intelligence Index
9.7
Coding Index
21.2
Agentic Index
0.8
Agentic work
AutomationBench-AA
Workflow automation with guardrail penalties
0.3%
Better than 5% of models compared
AA-Briefcase
Agentic knowledge work (Elo)
66 Elo
Better than 8% of models compared
GDPval-AA v2
Economically valuable tasks (Elo)
73 Elo
Better than 11% of models compared
Document reasoning
GDP.pdf
Professional PDF reasoning: all-pass rate
1.6%
Better than 12% of models compared
AA-LCR v1.1
Long context reasoning with updated grading
40.7%
Better than 36% of models compared
Reasoning
HLE
Humanity's Last Exam
4.7%
Better than 24% of models compared
IFBench
Instruction-following benchmark
41.0%
Better than 40% of models compared
CritPt
Research-level physics reasoning
0.0%
Coding
Terminal-Bench v4.0
Practical coding and terminal tasks
0.0%
Better than 16% of models compared
SciCode
Python programming for scientific computing
39.0%
Better than 23% of models compared
LiveCodeBench
Contamination-free coding benchmark
40.5%
Better than 48% of models compared
Math
AIME 2025
American Invitational Mathematics Examination 2025
41.0%
Better than 42% 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
81.9%
Better than 77% of models compared
AA-Omniscience Accuracy
Proportion of correctly answered questions
24.3%
AA-Omniscience Hallucination Rate
Rate of incorrect answers among non-correct responses
85.9%
Legacy benchmarks
GPQA Diamond (legacy)
Graduate-level scientific reasoning
65.5%
Better than 41% of models compared
Terminal-Bench Hard (legacy)
Agentic coding and terminal use
15.2%
Better than 52% of models compared
T²-Bench Telecom (legacy)
Conversational AI agents in dual-control scenarios
47.1%
Better than 51% of models compared
AA-LCR (unversioned / legacy)
Long context reasoning evaluation
40.7%
Better than 36% of models compared
GDPval-AA (unversioned / legacy)
Economically valuable tasks
0.0%
Last updated Oct 3, 2026
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