DeepSeek V4.1 Flash vs GPT-6.1 Sol: 2026 comparison

DeepSeek V4.1 Flash and GPT-6.1 Sol in brief

DeepSeek V4.1 Flash and GPT-6.1 Sol both provide large context windows, reasoning and tool calling. Their clearest differences involve token prices, weight availability and evaluation results. DeepSeek offers much lower listed rates, open weights and a substantially longer maximum output. GPT also accepts files and scores higher on every supplied benchmark shared by the two models. Your choice therefore depends on how you weigh spending, access to model weights and measured performance against the requirements of your own tasks.

DeepSeek V4.1 Flash : DeepSeek V4.1 Flash (DeepSeek): $0.02 / $1 per million tokens, 1M context, benchmarks, verdict and alternatives. Checked October 25, 2026.

GPT-6.1 Sol : GPT-6.1 Sol (OpenAI): $2 / $10 per million tokens, 1.1M context, benchmarks, verdict and alternatives. Checked October 25, 2026.

They are often weighed against each other by people who hesitate. This page lines up their features point by point (11 criteria), with no partnership with either publisher.

Comparison table

Criterion by criterion

For each criterion: why it matters, each one's value and who has the edge. A criterion without data on both sides is not decided.

Capabilities (Epoch AI index)

Epoch AI's capabilities index rolls many benchmarks into one number: the higher, the better the model does on hard tasks. DeepSeek V4.1 Flash : 155.9. GPT-6.1 Sol : 168.4. Edge: GPT-6.1 Sol.

Shared benchmarks

On the benchmarks both models took, we count each one's wins. DeepSeek V4.1 Flash : 0 of 4. GPT-6.1 Sol : 4 of 4. Edge: GPT-6.1 Sol.

Input price

The price of tokens sent to the model weighs heavily as soon as you send long documents or chat history. DeepSeek V4.1 Flash : $0.02 / M tokens. GPT-6.1 Sol : $2 / M tokens. Edge: DeepSeek V4.1 Flash.

Output price

Generated tokens usually cost several times more; they dominate the bill for writing and code. DeepSeek V4.1 Flash : $1 / M tokens. GPT-6.1 Sol : $10 / M tokens. Edge: DeepSeek V4.1 Flash.

Cost of a typical task

For a request of 2,000 input and 500 output tokens, repeated 30,000 times a month (1,000 a day). DeepSeek V4.1 Flash : $16 per month. GPT-6.1 Sol : $270 per month. Edge: DeepSeek V4.1 Flash.

Context window

The context window sets how much text the model reads at once: useful for long documents and agents. DeepSeek V4.1 Flash : 1M tokens. GPT-6.1 Sol : 1.1M tokens. Edge: GPT-6.1 Sol.

Max output

The maximum answer length matters to generate long code files or reports. DeepSeek V4.1 Flash : 943.7K tokens. GPT-6.1 Sol : 128K tokens. Edge: DeepSeek V4.1 Flash.

Accepted inputs

Text, images, files, audio: the more formats accepted, the more cases covered without extra tools. DeepSeek V4.1 Flash : 2. GPT-6.1 Sol : 3. Edge: GPT-6.1 Sol.

Tool calling

Essential for an agent: the model can call functions, APIs or MCP servers. DeepSeek V4.1 Flash : yes. GPT-6.1 Sol : yes. A tie on this point.

Open weights

Downloadable weights let you run the model yourself, without sending data to a provider. DeepSeek V4.1 Flash : yes. GPT-6.1 Sol : no. Edge: DeepSeek V4.1 Flash.

Release date

A newer model often has fresher data and better training techniques. DeepSeek V4.1 Flash : September 10, 2026. GPT-6.1 Sol : September 29, 2026. Edge: GPT-6.1 Sol.

In charts

Our analysis

DeepSeek V4.1 Flash was released on September 10, 2026, and accepts text and images. OpenAI released GPT-6.1 Sol on September 29, with files included alongside those input categories. Both support reasoning and tool calling, giving them a common functional basis for applications combining analysis with actions. DeepSeek’s publisher is listed in China and OpenAI in the United States. Those details identify the publishers without describing the operational terms of a particular service. Your required formats offer a more immediate distinction: file input is explicitly listed for GPT, whereas it does not appear in the supplied DeepSeek profile.

DeepSeek costs $0.02 per million input tokens and $1 per million output tokens, compared with $2 and $10 for GPT. GPT nevertheless leads on the shared evaluations: 95.4% versus 93.2% on GPQA, 93.7% versus 71.2% on FrontierMath, 100% versus 99.4% on AIME, and 73.9% versus 42.5% on SimpleQA. Its Epoch capability index is 168.37, against DeepSeek’s 155.93. GPT also has listed ARC1 and ARC2 results, while DeepSeek has no supplied scores for those tests. Missing results do not mean zero performance and do not support a direct comparison on either of those two additional benchmarks.

DeepSeek has open weights, while GPT does not. That distinction matters if access to the weights is part of your requirements, but neither supplied profile specifies a license. You therefore cannot assume unrestricted modification, redistribution or commercial use. Hardware requirements and deployment procedures are also unspecified, limiting what can be concluded about running a weight-based deployment. Neither profile describes data retention or any use of submitted content for training. Publisher location does not establish the actual processing location either. Weight availability and privacy are consequently separate questions, and the supplied information does not settle the latter for either model.

Their context windows are very close: 1,048,576 tokens for DeepSeek and 1,050,000 for GPT. Maximum output differs more substantially, at 943,718 tokens for DeepSeek versus 128,000 for GPT. The larger allowance may matter for long-generation applications, without guaranteeing consistency throughout an output or an acceptable completion time. Listed token rates also do not establish the total cost of a deployment using the weights, since infrastructure costs are not supplied. For everyday use, compare required formats, token spending and actual results together. Latency, throughput and availability remain unspecified, so those practical constraints require evidence beyond the information in these profiles.

Depending on your situation

The right choice depends on use. For a few common situations, here are the criteria that matter and who wins (DeepSeek V4.1 Flash value / GPT-6.1 Sol value).

High request volume

Here, DeepSeek V4.1 Flash has the edge: it wins 3 of the 3 criteria that matter in this situation, namely cost of a typical task ($16 per month / $270 per month), input price ($0.02 / M tokens / $2 / M tokens), and output price ($1 / M tokens / $10 / M tokens).

Hard tasks (reasoning, code)

Here, GPT-6.1 Sol has the edge: it wins 2 of the 3 criteria that matter in this situation, namely capabilities (epoch ai index) (155.9 / 168.4), shared benchmarks (0 of 4 / 4 of 4), and max output (943.7K tokens / 128K tokens).

Agents and long documents

Here, GPT-6.1 Sol has the edge: it wins 2 of the 3 criteria that matter in this situation, namely tool calling (yes / yes), context window (1M tokens / 1.1M tokens), and accepted inputs (2 / 3).

Maximum privacy

Here, DeepSeek V4.1 Flash has the edge: it wins 1 of the 1 criteria that matter in this situation, namely open weights (yes / no).

Which one to choose?

Choose DeepSeek V4.1 Flash if…

• you value input price ($0.02 / M tokens)
• you value output price ($1 / M tokens)
• you value cost of a typical task ($16 per month)
• you value max output (943.7K tokens)
• you value open weights (yes)

DeepSeek V4.1 Flash is a logical candidate when token prices, access to weights or outputs beyond 128,000 tokens are central requirements. Your application must also fit its listed text and image inputs. Assess its economic appeal alongside representative task results and the licensing and deployment conditions, which the supplied data does not specify sufficiently to support a complete deployment decision.

Choose GPT-6.1 Sol if…

• you value capabilities (epoch ai index) (168.4)
• you value shared benchmarks (4 of 4)
• you value context window (1.1M tokens)
• you value accepted inputs (3)
• you value release date (September 29, 2026)

GPT-6.1 Sol fits requirements that prioritize the supplied shared benchmark results, where it leads throughout, or explicitly listed file input support. Its context size remains close to DeepSeek’s. Given its higher token rates, evaluate whether the results it produces justify the additional spending in your application, particularly if the workload involves substantial input volumes or frequent output generation.

Strengths and limits of each

DeepSeek V4.1 Flash

• + Low price: cheaper than 72 % of paid models.
• + Very large context (1M tokens): long documents or a whole codebase fit in one request.
• + Open weights: self-hostable, your data stays with you.

GPT-6.1 Sol

• + Among the top 20% of models according to Epoch AI (ECI 168.4).
• + Very large context (1.1M tokens): long documents or a whole codebase fit in one request.
• + Agent-ready: tool calling and structured JSON output.
• − Expensive: costlier than 84 % of paid models.

The best test is still yours: take three real tasks from your day and give them to both for a week. The price calculator works out the gap on your own volume.

What about the others?

If neither suits you, look at Claude Opus 5.5, GPT-6 Astra, Claude Fable 5.1, Gemini 4 Argon, Claude Sonnet 5.5, and Claude Opus 5.

Related comparisons:

• Claude Opus 5.5 vs GPT-6.1 Sol
• Gemini 4 Argon vs GPT-6.1 Sol
• Mistral Large 4 vs GPT-6.1 Sol

Conclusion

DeepSeek stands out for low token prices, open weights and a much longer maximum output. GPT offers stronger results on the shared tests and adds files to its listed inputs. Begin with required formats and deployment constraints, then compare representative task results against observed spending. Open weights alone do not settle licensing, privacy or total operating costs, while benchmark leads do not establish the value of every individual workflow.