How much does DeepSeek V3.1 Terminus cost?

DeepSeek V3.1 Terminus costs $0.27/M input tokens and $1.00/M output tokens.

What is the context length of DeepSeek V3.1 Terminus?

DeepSeek V3.1 Terminus has a 163,840 token context window. It supports up to 163,840 completion tokens.

Does DeepSeek V3.1 Terminus support tool calling and structured outputs?

Yes. DeepSeek V3.1 Terminus accepts tools and tool_choice for function calling. It also supports structured outputs via a JSON schema in response_format.

Which providers serve DeepSeek V3.1 Terminus?

DeepSeek V3.1 Terminus is served by 1 provider on OpenRouter: SiliconFlow.

What other text models does DeepSeek have?

DeepSeek V4.1 Flash 1017, DeepSeek V4.1 Flash, DeepSeek V4 Flash Vision Exp and 9 more are other text models from DeepSeek.

When was DeepSeek V3.1 Terminus released?

DeepSeek V3.1 Terminus was released on September 22, 2025. Its knowledge cutoff is March 31, 2025.

DeepSeek: DeepSeek V3.1 Terminus

deepseek/deepseek-v3.1-terminus

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DeepSeek-V3.1 Terminus is an update to DeepSeek 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 performance in coding and search agents. It is a large hybrid reasoning model (671B parameters, 37B active) that supports both thinking and non-thinking modes. It extends the DeepSeek-V3 base with a two-phase long-context training process, reaching up to 128K tokens, and uses FP8 microscaling for efficient inference. Users can control the reasoning behaviour with the reasoning enabled boolean. Learn more in our docs

The model improves tool use, code generation, and reasoning efficiency, achieving performance comparable to DeepSeek-R1 on difficult benchmarks while responding more quickly. It supports structured tool calling, code agents, and search agents, making it suitable for research, coding, and agentic workflows.

Modalities

In / out price

$0.27 / $1per 1M

Context

164K

Released

Sep 22, 2025

Knowledge cutoff

Mar 2025

Providers

This model is hosted by one provider. OpenRouter forwards every request without routing.

Latency / throughput


	$0.27	$1.00	1.83s	21 tok/s	100.00%

Input $0.27Output $1.00
21 tok/s100.00% uptime

Pricing

The average price customers actually pay for this model, next to the prices providers post. Caching and discounts mean the price actually paid is often well below the listed one.

Weighted average

Input

$0.2699/M tokens

Output

$1/M tokens

DeepSeek V3.1 Terminus — Price history

Price history

No effective price data for this selection

		$0.270	$1.00	64.5%	100.0%

Effective in $0.270Effective out $1.00
Token share
100.0%

Uptime

Uptime is the percentage of the past 3 days that at least one provider was responding to requests. Availability is the percentage of time that inference was successfully served. OpenRouter continuously monitors and uses the next-best provider when one returns an error.

Uptime (3d)

100.00%

Availability (3d)

99.08%

Availability over the last 3 days

Last 72 hours

Availability 99.08%

3 Days Ago2 Days AgoYesterdayNow

Availability over the last 24 hours

OpenRouter Availability

99.30%

When an error occurs in an upstream provider, we can recover by routing to another healthy provider, if your request filters allow it. You can access per-provider uptime data programmatically through the Endpoints API. Learn more about our load balancing and provider routing options.

Benchmarks

Scores on standardized evaluations and this model’s own outputs on shared benchmark prompts. Higher percentages are better — and each rank shows where this model lands among the models evaluated on that source.

Benchmark score summary for DeepSeek: DeepSeek V3.1 Terminus (Artificial Analysis and Design Arena)
Source	Benchmark	Score
Artificial Analysis	DeepSeek V3.1 Terminus (Reasoning) Coding Index	43.5
Artificial Analysis	DeepSeek V3.1 Terminus (Reasoning) Agentic Index	6.8
Artificial Analysis	DeepSeek V3.1 Terminus (Reasoning) GPQA Diamond	79.2%
Artificial Analysis	DeepSeek V3.1 Terminus (Reasoning) HLE	16.4%
Artificial Analysis	DeepSeek V3.1 Terminus (Reasoning) IFBench	57.0%
Artificial Analysis	DeepSeek V3.1 Terminus (Reasoning) τ²-Bench Telecom	37.1%
Artificial Analysis	DeepSeek V3.1 Terminus (Reasoning) AA-LCR	69.3%
Artificial Analysis	DeepSeek V3.1 Terminus (Reasoning) τ-Bench Banking	21.0%
Artificial Analysis	DeepSeek V3.1 Terminus (Reasoning) GDPval-AA	10.9%
Artificial Analysis	DeepSeek V3.1 Terminus (Reasoning) CritPt	1.7%
Artificial Analysis	DeepSeek V3.1 Terminus (Reasoning) LiveCodeBench	79.8%
Artificial Analysis	DeepSeek V3.1 Terminus (Reasoning) SciCode	38.0%
Artificial Analysis	DeepSeek V3.1 Terminus (Reasoning) Terminal-Bench Hard	30.3%
Artificial Analysis	DeepSeek V3.1 Terminus (Reasoning) Terminal-Bench 4.0	0.0%
Artificial Analysis	DeepSeek V3.1 Terminus (Reasoning) Terminal-Bench 2.1	44.9%
Artificial Analysis	DeepSeek V3.1 Terminus (Reasoning) MMLU-Pro	85.1%
Artificial Analysis	DeepSeek V3.1 Terminus (Reasoning) AIME 2025	89.7%
Artificial Analysis	DeepSeek V3.1 Terminus (Non-reasoning) GPQA Diamond	75.1%
Artificial Analysis	DeepSeek V3.1 Terminus (Non-reasoning) HLE	8.7%
Artificial Analysis	DeepSeek V3.1 Terminus (Non-reasoning) IFBench	41.2%
Artificial Analysis	DeepSeek V3.1 Terminus (Non-reasoning) τ²-Bench Telecom	37.1%
Artificial Analysis	DeepSeek V3.1 Terminus (Non-reasoning) AA-LCR	45.3%
Artificial Analysis	DeepSeek V3.1 Terminus (Non-reasoning) CritPt	0.0%
Artificial Analysis	DeepSeek V3.1 Terminus (Non-reasoning) LiveCodeBench	52.9%
Artificial Analysis	DeepSeek V3.1 Terminus (Non-reasoning) Terminal-Bench Hard	31.8%
Artificial Analysis	DeepSeek V3.1 Terminus (Non-reasoning) MMLU-Pro	83.6%
Artificial Analysis	DeepSeek V3.1 Terminus (Non-reasoning) AIME 2025	53.7%
Design Arena	DeepSeek-V3.1 (Thinking) Models Arena 3D Elo	1120
Design Arena	DeepSeek-V3.1 (Thinking) Models Arena Code Categories Elo	1165
Design Arena	DeepSeek-V3.1 (Thinking) Models Arena Data Visualization Elo	1164
Design Arena	DeepSeek-V3.1 (Thinking) Models Arena Game Development Elo	1120
Design Arena	DeepSeek-V3.1 (Thinking) Models Arena SVG Elo	1054
Design Arena	DeepSeek-V3.1 (Thinking) Models Arena UI Component Elo	1172
Design Arena	DeepSeek-V3.1 (Thinking) Models Arena Website Elo	1179

Apps

Public apps that send the most traffic to this model. Good signal for what real production workloads look like — and a hint at which use cases this model is best suited for.

Janitor AI

Janitor AI is a chatbot platform where users create and chat with custom AI characters for interactive roleplay, storytelling, and immersive fiction.

5.39Btokens

Chub AI

GenAI for everyone

2.84Btokens

SillyTavern

SillyTavern is the LLM frontend for power users, a chat interface that connects to any model and gives deep control through character creation, roleplay, and prompt customization.

905Mtokens

Sophia's LoreBary

Organize and enhance your roleplay creations.

248Mtokens

Dwemer Dynamics

new

183Mtokens

Top 5 Apps

79.2M

Rest of OpenRouter

74.3M

Not currently available through EU in-region routing. In-region routing docs

More models from DeepSeek

DeepSeek V4.1 Flash 1017

DeepSeek V4.1 Flash 1017 is the October 17, 2026 snapshot of DeepSeek V4.1 Flash, served by DeepSeek under the deepseek-flash API model name. V4.1 Flash is a sparse mixture-of-experts model from DeepSeek, and the first built on the company's Causal Encoder-Decoder (CED) architecture. It activates 8B parameters on input and 16B on output from a 552B-parameter backbone, an asymmetric split that keeps per-token compute low relative to the model's total size. Image understanding is native to the architecture, with visual and text embeddings trained jointly from the start of pre-training rather than added afterward as in the earlier experimental V4 Flash Vision Exp.

It is suited for coding, terminal, and computer-use agents, along with long-horizon tasks that must run to completion across many steps and long-context analysis. Compressed KV caching cuts cache memory to roughly a quarter of the previous Flash generation, significantly reducing costs on agentic workloads. DeepSeek positions it as the cost-efficient tier of the V4.1 family and reports that it exceeds V4 Pro on performance, speed, and task completion time.

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DeepSeek V4.1 Flash 1017 (batch)

Text1.0M context$0.05 / $0.15

DeepSeek Pro Latest

This model always redirects to the latest model in the DeepSeek Pro family.

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DeepSeek Flash Latest

This model always redirects to the latest model in the DeepSeek Flash family.

Text1.0M context

DeepSeek V4.1 Flash

DeepSeek V4.1 Flash is a sparse mixture-of-experts model from DeepSeek, and the first built on the company's Causal Encoder-Decoder (CED) architecture. It activates 8B parameters on input and 16B on output from a 552B-parameter backbone, an asymmetric split that keeps per-token compute low relative to the model's total size. Image understanding is native to the architecture, with visual and text embeddings trained jointly from the start of pre-training rather than added afterward as in the earlier experimental V4 Flash Vision Exp.

Text1.0M context$0.0507 / $0.2028

DeepSeek V4.1 Flash (batch)

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DeepSeek V4 Flash Vision Exp

DeepSeek V4 Flash Vision Exp is an experimental vision-enabled version of DeepSeek V4 Flash 0731 from DeepSeek, adding image understanding while matching the base model on text capabilities including agents, reasoning, and world knowledge. It is a sparse mixture-of-experts model with 13B active parameters out of 284B total.

It is suited for document and chart understanding, visual question answering, and multimodal agent workflows that interleave text and images.

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DeepSeek V4 Pro 0813

DeepSeek V4 Pro 0813 is a large-scale mixture-of-experts model from DeepSeek. This is the GA release of DeepSeek V4 Pro.

Text1.0M context$0.0999 / $8

DeepSeek V4 Flash Latest

This model always redirects to the latest model in the DeepSeek V4 Flash family.

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DeepSeek V4 Flash 0731

DeepSeek V4 Flash 0731 is a sparse mixture-of-experts model from DeepSeek, with 13B active parameters out of 284B total. This re-post-trained revision is suited for coding, reasoning, and agent workflows. This is the GA release of DeepSeek V4 Flash.

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DeepSeek V4 Flash 0731 (batch)

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DeepSeek V4 Pro 0423

DeepSeek V4 Pro is a large-scale Mixture-of-Experts model from DeepSeek with 1.6T total parameters and 49B activated parameters, supporting a 1M-token context window. It is designed for advanced reasoning, coding, and long-horizon agent workflows, with strong performance across knowledge, math, and software engineering benchmarks.

Built on the same architecture as DeepSeek V4 Flash, it introduces a hybrid attention system for efficient long-context processing. Reasoning efforts high and xhigh are supported; xhigh maps to max reasoning. It is well suited for complex workloads such as full-codebase analysis, multi-step automation, and large-scale information synthesis, where both capability and efficiency are critical

Text1.0M context$0.353 / $0.7061

DeepSeek V4 Flash 0423

DeepSeek V4 Flash is an efficiency-optimized Mixture-of-Experts model from DeepSeek with 284B total parameters and 13B activated parameters, supporting a 1M-token context window. It is designed for fast inference and high-throughput workloads, while maintaining strong reasoning and coding performance.

The model includes hybrid attention for efficient long-context processing. Reasoning efforts high and xhigh are supported; xhigh maps to max reasoning. It is well suited for applications such as coding assistants, chat systems, and agent workflows where responsiveness and cost efficiency are important.

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DeepSeek V3.2 Speciale

DeepSeek-V3.2-Speciale is a high-compute variant of DeepSeek-V3.2 optimized for maximum reasoning and agentic performance. It builds on DeepSeek Sparse Attention (DSA) for efficient long-context processing, then scales post-training reinforcement learning to push capability beyond the base model. Reported evaluations place Speciale ahead of GPT-5 on difficult reasoning workloads, with proficiency comparable to Gemini-3.0-Pro, while retaining strong coding and tool-use reliability. Like V3.2, it benefits from a large-scale agentic task synthesis pipeline that improves compliance and generalization in interactive environments.

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DeepSeek V3.2

DeepSeek-V3.2 is a large language model designed to harmonize high computational efficiency with strong reasoning and agentic tool-use performance. It introduces DeepSeek Sparse Attention (DSA), a fine-grained sparse attention mechanism that reduces training and inference cost while preserving quality in long-context scenarios. A scalable reinforcement learning post-training framework further improves reasoning, with reported performance in the GPT-5 class, and the model has demonstrated gold-medal results on the 2025 IMO and IOI. V3.2 also uses a large-scale agentic task synthesis pipeline to better integrate reasoning into tool-use settings, boosting compliance and generalization in interactive environments.

Users can control the reasoning behaviour with the reasoning enabled boolean. Learn more in our docs

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DeepSeek V3.2 Exp

DeepSeek-V3.2-Exp is an experimental large language model released by DeepSeek as an intermediate step between V3.1 and future architectures. It introduces DeepSeek Sparse Attention (DSA), a fine-grained sparse attention mechanism designed to improve training and inference efficiency in long-context scenarios while maintaining output quality. Users can control the reasoning behaviour with the reasoning enabled boolean. Learn more in our docs

The model was trained under conditions aligned with V3.1-Terminus to enable direct comparison. Benchmarking shows performance roughly on par with V3.1 across reasoning, coding, and agentic tool-use tasks, with minor tradeoffs and gains depending on the domain. This release focuses on validating architectural optimizations for extended context lengths rather than advancing raw task accuracy, making it primarily a research-oriented model for exploring efficient transformer designs.

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DeepSeek V3.1

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It succeeds the DeepSeek V3-0324 model and performs well on a variety of tasks.

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DeepSeek V3.1 Base

This is a base model, trained only for raw next-token prediction. Unlike instruct/chat models, it has not been fine-tuned to follow user instructions. Prompts need to be written more like training text or examples rather than simple requests (e.g., “Translate the following sentence…” instead of just “Translate this”).

DeepSeek-V3.1 Base is a 671B parameter open Mixture-of-Experts (MoE) language model with 37B active parameters per forward pass and a context length of 128K tokens. Trained on 14.8T tokens using FP8 mixed precision, it achieves high training efficiency and stability, with strong performance across language, reasoning, math, and coding tasks.

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R1 Distill Qwen 7B

DeepSeek-R1-Distill-Qwen-7B is a 7 billion parameter dense language model distilled from DeepSeek-R1, leveraging reinforcement learning-enhanced reasoning data generated by DeepSeek's larger models. The distillation process transfers advanced reasoning, math, and code capabilities into a smaller, more efficient model architecture based on Qwen2.5-Math-7B. This model demonstrates strong performance across mathematical benchmarks (92.8% pass@1 on MATH-500), coding tasks (Codeforces rating 1189), and general reasoning (49.1% pass@1 on GPQA Diamond), achieving competitive accuracy relative to larger models while maintaining smaller inference costs.

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DeepSeek-R1-0528 is a lightly upgraded release of DeepSeek R1 that taps more compute and smarter post-training tricks, pushing its reasoning and inference to the brink of flagship models like O3 and Gemini 2.5 Pro. It now tops math, programming, and logic leaderboards, showcasing a step-change in depth-of-thought. The distilled variant, DeepSeek-R1-0528-Qwen3-8B, transfers this chain-of-thought into an 8 B-parameter form, beating standard Qwen3 8B by +10 pp and tying the 235 B “thinking” giant on AIME 2024.

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DeepSeek: DeepSeek V3.1 Terminus

Providers