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DeepSeek V3.1 Terminus (Thinking)

deepseek-ai/DeepSeek-V3.1-Terminus:thinking
Provider logo

DeepSeek V3.1 Terminus (Thinking)

deepseek-ai/DeepSeek-V3.1-Terminus:thinking

Thinking-enabled DeepSeek-V3.1-Terminus with improved language consistency, upgraded Code/Search Agents, and stronger stability and reliability versus V3.1. FP8.

Context Window

128.0K

Max Output

65.5K

Avg output tokens (7d)

548 tokens

48%

Input Price (Auto)

$0.25/1M

Output Price (Auto)

$0.70/1M

Cache Read (Auto)

$0.13/1M

Capabilities

Benchmarks

Performance metrics and benchmarks

Sourced from Artificial Analysis.

Intelligence Index

15.4

Better than 60% of models compared

Coding Index

43.5

Better than 50% of models compared

Agentic work

AutomationBench-AA

Workflow automation with guardrail penalties

2.7%

Better than 26% of models compared

AA-Briefcase

Agentic knowledge work (Elo)

464 Elo

Better than 24% of models compared

GDPval-AA v2

Economically valuable tasks (Elo)

826 Elo

Better than 38% of models compared

Document reasoning

GDP.pdf

Professional PDF reasoning: all-pass rate

5.4%

Better than 31% of models compared

AA-LCR v1.1

Long context reasoning with updated grading

69.3%

Better than 65% of models compared

Reasoning

HLE

Humanity's Last Exam

16.4%

Better than 67% of models compared

IFBench

Instruction-following benchmark

57.0%

Better than 68% 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

38.0%

Better than 21% of models compared

LiveCodeBench

Contamination-free coding benchmark

79.8%

Better than 92% of models compared

Math

AIME 2025

American Invitational Mathematics Examination 2025

89.7%

Better than 90% of models compared

Knowledge

MMLU-Pro

Professional and academic subject knowledge

85.1%

Better than 91% of models compared

Legacy benchmarks

GPQA Diamond (legacy)

Graduate-level scientific reasoning

79.2%

Better than 66% of models compared

Terminal-Bench Hard (legacy)

Agentic coding and terminal use

30.3%

Better than 72% of models compared

T²-Bench Telecom (legacy)

Conversational AI agents in dual-control scenarios

37.1%

Better than 45% of models compared

AA-LCR (unversioned / legacy)

Long context reasoning evaluation

69.3%

Better than 65% of models compared

Last updated Sep 13, 2026

Artificial Analysis

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