DeepSeek Flash vs GPT-6.1-sol: What Five Everyday Tasks Showed

The most obvious difference in this small comparison was not correctness. It was pace and presentation: DeepSeek Flash responded sooner, while gpt-6.1-sol produced more concise answers.

That sounds like a straightforward trade-off. But an API compatibility failure, an unresolved token accounting question, and the new DeepSeek V4.1 Flash 1017 release make the story more interesting—and less definitive than a leaderboard.

The five-task snapshot

gpt-6.1-sol took about one second longer on average in this run. Calculated from the rounded averages, its response time was approximately 59% higher. Its slowest reported answer was the translation task.

These are reported response times, not a verified time-to-first-token or throughput benchmark. Region, network conditions, endpoint, reasoning settings, and measurement boundaries were not controlled in the supplied record. Five tasks also give little visibility into reliability or longer-running work.

The difference was in how the answers arrived

The tester found DeepSeek more expansive and gpt-6.1-sol more restrained. In the coding task, the report describes extra handling and explanation from DeepSeek, while gpt-6.1-sol used casefold and type annotations. Those choices are worth inspecting, but neither is automatically better without the original requirements.

Translation produced a similar preference: the tester favored gpt-6.1-sol’s phrasing. That is a useful account of editing effort, rather than evidence of general linguistic superiority. Both models reportedly followed the formatting instructions.

For this tester, the practical question became: would a quicker first answer save more time, or would a more concise answer need less editing? The answer depends on the job. This sample suggests what to test next; it does not establish a universal winner.

930 tokens versus 248 is not yet a cost comparison

The reported token counts differ by 3.75×. It is tempting to turn that into a claim about hidden reasoning or a bill. The available record does not support either conclusion.

We still need the original usage fields to establish whether reasoning tokens are included and whether both providers count the same categories. Different tokenizers also mean token totals are not a direct measure of answer length. Actual cost additionally depends on input tokens, caching, rates, and any separately billed tools.

For now, the defensible observation is simple: the summary reports higher token usage for DeepSeek. It does not show that DeepSeek cost 3.75 times as much.

What the 1017 update adds to the picture

DeepSeek’s October 17 release notes say that V4.1 Flash has been updated to 1017 across its web, app, and API offerings, with unchanged model API pricing. The API name remains deepseek-flash. This is a moving alias: the supplied test record does not establish that the comparison used a pinned 1017 snapshot. Official release notes.

The model card reports the following changes against the previous V4.1 Flash:

Official results, not our measurements. Terminal-Bench uses the Claude Code harness; DeepSWE uses Mini-SWE-V2. HLE figures above are not the text-only subset. Source: DeepSeek model card.

DeepSeek also reports reduced runtime and output length relative to its previous model, alongside benchmark gains. This does not conflict with our tester reporting more tokens than gpt-6.1-sol: the comparisons have different baselines. The release notes recommend DeepSeek Harness 0.2.1-rc.1 or later. These are vendor claims, not findings from the five-task test. Release details.

The 1017 weights are available under the MIT license. Access to weights is separate from the infrastructure required to run them. Weights and license.

Search and Fetch add another cost category

DeepSeek now exposes Search for finding web pages and Fetch for retrieving page content. Its English documentation lists each at $0.75 per 1,000 successful requests that return results or content; failures and empty search results are not billed.

The documentation says server-side Search/Fetch calls made through DeepSeek Harness, Claude Code, or Codex begin per-request billing on November 1, 2026, at 00:00 Beijing time. This transition concerns those server-side tool calls, not a blanket free period for every direct API request. These charges are separate from model tokens, and the supplied comparison does not establish whether either tool was used. Official Search & Fetch guide.

An integration failure that looked like silence

Before the comparison, the tester reported repeated HTTP 400 errors when using gpt-6.1-sol in OpenClaw. The error identified a combination of function tools and reasoning effort on Chat Completions as unsupported for that deployment. Trying the suggested none value reportedly produced a second rejection.

Switching the provider’s API mode to openai-responses restored a basic text reply, according to the report. This establishes a reported recovery of text generation; the supplied evidence does not demonstrate a complete tool-call round trip. It is a deployment-specific troubleshooting account, not a universal configuration rule for gpt-6.1-sol.

The lesson is useful nonetheless: a silent assistant can be a failed request rather than a slow model. Endpoint compatibility and error handling belong in any practical evaluation.

What we would take from this test

DeepSeek was quicker in this small sample. gpt-6.1-sol’s more concise answers were preferred by the tester. Both passed the tester’s five manual checks. That is enough to motivate a larger trial, but not enough to rank their general coding ability, reasoning quality, or cost efficiency.

The next comparison should preserve complete prompts and responses, record model versions and reasoning settings, repeat each task, and distinguish first-token latency from completion time. Token categories and actual billed cost should be reported separately.

Until then, treat these results as a practical starting point: test speed on the tasks you repeat, and judge output quality by how much work remains after the answer arrives.