If you want to know which model is stronger overall on benchmarks, the honest answer is: there is not enough public, independent, apples-to-apples evidence to make a sweeping claim. The available picture is still fragmented: one SWE-bench Verified result from a third-party article , Artificial Analysis feature and reliability data , and OpenAI’s own API and safety documentation .

What the public sources actually establish

DeepSeek has an official DeepSeek-V4 Preview Release page dated 2026/04/24 . OpenAI introduced GPT-5.5 on Apr. 23, 2026, and noted that GPT-5.5 and GPT-5.5 Pro became available in the API on Apr. 24, 2026 . The two releases are close in timing, but not equal in the amount of public deployment detail available.

One detail is worth pausing on: OpenAI’s API docs list GPT-5.5 with a 1M-token context window , while the Artificial Analysis comparison page shows GPT-5.5 high at 922k tokens and DeepSeek V4 Pro high at 1000k tokens . That does not necessarily mean one source is wrong; it means you should not mix numbers from different pages unless you have checked the exact model variant, reasoning level and measurement definition.

Which benchmarks are most useful?

SWE-bench Verified: useful for coding, not the final word

A third-party o-mega article reports GPT-5.5 at 88.7% on SWE-bench Verified versus 80.6% for DeepSeek V4-Pro, an 8.1-point gap . If your main workload is software engineering, that is a meaningful signal.

But a single SWE-bench number should not replace your own evaluation. Coding-agent results can shift with prompt design, reasoning level, tool access, retry policy, test execution, patch format and the scoring harness. In practice, the 88.7% versus 80.6% result is a reason to prioritize GPT-5.5 in your coding evals — not proof that GPT-5.5 wins every task .

OpenAI’s system card: broad coverage, but not a DeepSeek head-to-head

OpenAI’s Deployment Safety Hub says GPT-5.5 controllability is measured with CoT-Control, an evaluation suite of more than 13,000 tasks built from benchmarks including GPQA, MMLU-Pro, HLE, BFCL and SWE-Bench Verified . That helps explain the breadth of OpenAI’s internal evaluation setup.

It does not, by itself, tell you whether GPT-5.5 beats DeepSeek V4 on GPQA, MMLU-Pro or SWE-Bench Verified. It is evidence about how GPT-5.5 was evaluated, not a public head-to-head leaderboard against DeepSeek .

AA-Omniscience: DeepSeek improves on knowledge, but hallucination is the warning light

Artificial Analysis reports that DeepSeek V4 Pro Max scores -10 on AA-Omniscience, an 11-point improvement over V3.2 Reasoning at -21; DeepSeek V4 Flash Max scores -23 . The same source reports very high hallucination rates: 94% for DeepSeek V4 Pro and 96% for V4 Flash, meaning that when the model does not know the answer, it nearly always responds anyway .

That matters for any product where unsupported confidence is costly: internal knowledge search, legal or financial document review, medical information workflows, compliance tasks or citation-heavy research. DeepSeek V4 Pro may be attractive because of open weights and long context, but factual QA should be paired with retrieval, citation checks, source verification and human review where appropriate .

Which model should you choose?

Choose GPT-5.5 for clearer production API deployment

GPT-5.5 is the safer starting point if your priority is fast integration, predictable deployment limits and official tool support. OpenAI’s API documentation lists the model ID, context window, max output, pricing, knowledge cutoff of Dec. 1, 2025, and support for Functions, Web search, File search and Computer use .

It is also the stronger first candidate for coding-agent evaluations if you are starting from the public SWE-bench signal available today . Still, the right move is to rerun the test on your own repositories, build system and agent harness.

Choose DeepSeek V4 Pro if open weights are non-negotiable

DeepSeek V4 Pro is the model to test first if you require open weights, want more control over infrastructure, or do not want to rely only on a closed API. Artificial Analysis describes DeepSeek V4 Pro as an open-weights model released in April 2026, with text input/output and a 1m-token context window .

The trade-off is factual reliability. Because Artificial Analysis reports a 94% hallucination rate for DeepSeek V4 Pro in AA-Omniscience, high-stakes factual workflows should not let the model answer unchecked .

Choose GPT-5.5 for image input or official tool-use workflows

In the Artificial Analysis comparison of DeepSeek V4 Pro high versus GPT-5.5 high, GPT-5.5 high is listed as supporting image input, while DeepSeek V4 Pro high is not . Add OpenAI’s documented support for Functions, Web search, File search and Computer use, and the current public evidence favors GPT-5.5 for multimodal or agentic tool-use workflows .

How to benchmark them properly before committing

Before routing production traffic or choosing a default model, run your own benchmark under matched conditions:

1. Lock the exact model and reasoning level. OpenAI lists reasoning levels for GPT-5.5 including none, low, medium, high and xhigh . Artificial Analysis also separates comparisons across low, medium and high configurations .
2. Use the same prompts, data and harness. Do not compare one model with a carefully tuned prompt against another with a raw prompt.
3. Keep tool policy equal. Coding-agent results can change dramatically if one model gets more retries, test access or file-editing privileges.
4. Measure more than accuracy. Track format failures, output stability, token cost, latency and the share of answers requiring human review.
5. Run a hallucination test. This is especially important for DeepSeek V4 Pro and V4 Flash given the AA-Omniscience hallucination figures .
6. Use real product data. If your customers use multiple languages, specialized documents or large codebases, include those in the evaluation.

Final verdict

GPT-5.5 is the more practical default if your goal is production API use, coding agents with official tools, image input, or clear published limits and pricing . DeepSeek V4 Pro is worth testing if open weights are a hard requirement and you are prepared to build verification layers around factual answers .

So, does DeepSeek V4 or GPT-5.5 win the benchmarks? Based on the public evidence available here, the careful answer is: not conclusively across the board. GPT-5.5 has the stronger cited SWE-bench Verified signal from one third-party source and the clearer API/tooling documentation . DeepSeek V4 Pro stands out for open weights and long context . The right choice is the one that wins under your workload, your harness and your risk tolerance.

If you want to know which model is stronger overall on benchmarks, the honest answer is: there is not enough public, independent, apples-to-apples evidence to make a sweeping claim. The available picture is still fragmented: one SWE-bench Verified result from a third-party article , Artificial Analysis feature and reliability data , and OpenAI’s own API and safety documentation .

What the public sources actually establish

DeepSeek has an official DeepSeek-V4 Preview Release page dated 2026/04/24 . OpenAI introduced GPT-5.5 on Apr. 23, 2026, and noted that GPT-5.5 and GPT-5.5 Pro became available in the API on Apr. 24, 2026 . The two releases are close in timing, but not equal in the amount of public deployment detail available.

One detail is worth pausing on: OpenAI’s API docs list GPT-5.5 with a 1M-token context window , while the Artificial Analysis comparison page shows GPT-5.5 high at 922k tokens and DeepSeek V4 Pro high at 1000k tokens . That does not necessarily mean one source is wrong; it means you should not mix numbers from different pages unless you have checked the exact model variant, reasoning level and measurement definition.

Which benchmarks are most useful?

SWE-bench Verified: useful for coding, not the final word

A third-party o-mega article reports GPT-5.5 at 88.7% on SWE-bench Verified versus 80.6% for DeepSeek V4-Pro, an 8.1-point gap . If your main workload is software engineering, that is a meaningful signal.

But a single SWE-bench number should not replace your own evaluation. Coding-agent results can shift with prompt design, reasoning level, tool access, retry policy, test execution, patch format and the scoring harness. In practice, the 88.7% versus 80.6% result is a reason to prioritize GPT-5.5 in your coding evals — not proof that GPT-5.5 wins every task .

OpenAI’s system card: broad coverage, but not a DeepSeek head-to-head

OpenAI’s Deployment Safety Hub says GPT-5.5 controllability is measured with CoT-Control, an evaluation suite of more than 13,000 tasks built from benchmarks including GPQA, MMLU-Pro, HLE, BFCL and SWE-Bench Verified . That helps explain the breadth of OpenAI’s internal evaluation setup.

It does not, by itself, tell you whether GPT-5.5 beats DeepSeek V4 on GPQA, MMLU-Pro or SWE-Bench Verified. It is evidence about how GPT-5.5 was evaluated, not a public head-to-head leaderboard against DeepSeek .

AA-Omniscience: DeepSeek improves on knowledge, but hallucination is the warning light

Artificial Analysis reports that DeepSeek V4 Pro Max scores -10 on AA-Omniscience, an 11-point improvement over V3.2 Reasoning at -21; DeepSeek V4 Flash Max scores -23 . The same source reports very high hallucination rates: 94% for DeepSeek V4 Pro and 96% for V4 Flash, meaning that when the model does not know the answer, it nearly always responds anyway .

That matters for any product where unsupported confidence is costly: internal knowledge search, legal or financial document review, medical information workflows, compliance tasks or citation-heavy research. DeepSeek V4 Pro may be attractive because of open weights and long context, but factual QA should be paired with retrieval, citation checks, source verification and human review where appropriate .

Which model should you choose?

Choose GPT-5.5 for clearer production API deployment

GPT-5.5 is the safer starting point if your priority is fast integration, predictable deployment limits and official tool support. OpenAI’s API documentation lists the model ID, context window, max output, pricing, knowledge cutoff of Dec. 1, 2025, and support for Functions, Web search, File search and Computer use .

It is also the stronger first candidate for coding-agent evaluations if you are starting from the public SWE-bench signal available today . Still, the right move is to rerun the test on your own repositories, build system and agent harness.

Choose DeepSeek V4 Pro if open weights are non-negotiable

DeepSeek V4 Pro is the model to test first if you require open weights, want more control over infrastructure, or do not want to rely only on a closed API. Artificial Analysis describes DeepSeek V4 Pro as an open-weights model released in April 2026, with text input/output and a 1m-token context window .

The trade-off is factual reliability. Because Artificial Analysis reports a 94% hallucination rate for DeepSeek V4 Pro in AA-Omniscience, high-stakes factual workflows should not let the model answer unchecked .

Choose GPT-5.5 for image input or official tool-use workflows

In the Artificial Analysis comparison of DeepSeek V4 Pro high versus GPT-5.5 high, GPT-5.5 high is listed as supporting image input, while DeepSeek V4 Pro high is not . Add OpenAI’s documented support for Functions, Web search, File search and Computer use, and the current public evidence favors GPT-5.5 for multimodal or agentic tool-use workflows .

How to benchmark them properly before committing

Before routing production traffic or choosing a default model, run your own benchmark under matched conditions:

1. Lock the exact model and reasoning level. OpenAI lists reasoning levels for GPT-5.5 including none, low, medium, high and xhigh . Artificial Analysis also separates comparisons across low, medium and high configurations .
2. Use the same prompts, data and harness. Do not compare one model with a carefully tuned prompt against another with a raw prompt.
3. Keep tool policy equal. Coding-agent results can change dramatically if one model gets more retries, test access or file-editing privileges.
4. Measure more than accuracy. Track format failures, output stability, token cost, latency and the share of answers requiring human review.
5. Run a hallucination test. This is especially important for DeepSeek V4 Pro and V4 Flash given the AA-Omniscience hallucination figures .
6. Use real product data. If your customers use multiple languages, specialized documents or large codebases, include those in the evaluation.

Final verdict

GPT-5.5 is the more practical default if your goal is production API use, coding agents with official tools, image input, or clear published limits and pricing . DeepSeek V4 Pro is worth testing if open weights are a hard requirement and you are prepared to build verification layers around factual answers .

So, does DeepSeek V4 or GPT-5.5 win the benchmarks? Based on the public evidence available here, the careful answer is: not conclusively across the board. GPT-5.5 has the stronger cited SWE-bench Verified signal from one third-party source and the clearer API/tooling documentation . DeepSeek V4 Pro stands out for open weights and long context . The right choice is the one that wins under your workload, your harness and your risk tolerance.