V3.1 Terminus
DeepSeek · released Sep 22, 2025
DeepSeek-V3.1 Terminus is an update to [DeepSeek V3.1](/deepseek/deepseek-chat-v3.1) that maintains the model's original capabilities while addressing issues reported by users, including language consistency and agent capabilities, further optimizing the model's...
Specification
- Context window
- 164K
- Max output
- 33K
- Knowledge cutoff
- Mar 31, 2025
- Parameters
- 684.5B
- Licence
- mit
- Serving providers
- 5
- Moderated
- No
- Uptime
- 100.0%
Intelligence
21.7
62th percentile
Coding
—
Coding Index
Agentic
—
Agentic Index
Output speed
55 t/s
Median across providers
Latency
803ms
Time to first token
Cost per task
$0.09
Estimated
Benchmarks
Where the score comes from
The Intelligence Index is a composite. These are the underlying evaluations this model was actually measured on.
Evaluation scores
Percentage correct · higher is better
- MMLU-Pro83.6%
- GPQA Diamond75.1%
- AIME 202553.7%
- LiveCodeBench52.9%
- AA-LCR (long context)45.0%
- IFBench41.2%
- τ²-bench (Telecom)37.1%
- SciCode32.1%
- Terminal-Bench Hard31.8%
- Humanity's Last Exam8.7%
An evaluation missing from this list was not run for this model — it is not a zero.
View as table
| Evaluation | Score |
|---|---|
| MMLU-Pro | 83.6% |
| GPQA Diamond | 75.1% |
| AIME 2025 | 53.7% |
| LiveCodeBench | 52.9% |
| AA-LCR (long context) | 45.0% |
| IFBench | 41.2% |
| τ²-bench (Telecom) | 37.1% |
| SciCode | 32.1% |
| Terminal-Bench Hard | 31.8% |
| Humanity's Last Exam | 8.7% |
Against its peers
Intelligence Index · this model highlighted, nearest peers in grey
- GLM 4.7 FlashZ.ai23.3
- Mercury 2Inception21.9
- Qwen3.5-9BQwen21.8
- V3.1 TerminusDeepSeek21.7
- Qwen3 Coder NextQwen21.3
- R1DeepSeek20.4
- North Mini Code (free)Cohere20.2
- GPT-5 NanoOpenAI20.1
Peers are the models sitting closest on the Intelligence Index — the set you would realistically choose between.
View as table
| Model | Intelligence |
|---|---|
| GLM 4.7 Flash | 23.3 |
| Mercury 2 | 21.9 |
| Qwen3.5-9B | 21.8 |
| V3.1 Terminus | 21.7 |
| Qwen3 Coder Next | 21.3 |
| R1 | 20.4 |
| North Mini Code (free) | 20.2 |
| GPT-5 Nano | 20.1 |
Percentile among all indexed models
Pricing
What it costs to run
List prices per million tokens, plus what one representative task works out to.
List price
- Input / 1M tokens
- $0.27
- Output / 1M tokens
- $1
- Cached input / 1M
- $0.135
- Blended 3:1
- $0.453
One task, estimated
$0.09
- Input tokens
- 50,000
- Output tokens
- 80,000
- Profile
- Reasoning
Estimated from list pricing: 50K input tokens plus 80K output tokens for reasoning models (25K for non-reasoning).
Serving providers
Speed and latency figures are medians across these providers, so a widely-served model reports a blend rather than any single endpoint.