# DeepSeek V4 Pro: 80.6% SWE-Bench at $0.87/M Output

> Source: <https://byteiota.com/deepseek-v4-pro-80-6-swe-bench-at-0-87-m-output/>
> Published: 2026-07-29 22:08:30+00:00

DeepSeek V4 Pro scores 80.6% on SWE-bench Verified — tied with Gemini 3.1 Pro and the highest score any open-weight model has posted. It ships a 1-million-token context window, runs under the MIT license, and costs $0.87 per million output tokens. That is 28.7 times cheaper than Claude Opus 4.8. It has been available since April 24, 2026, and most engineering teams have not evaluated it yet.

## What V4 Pro Actually Is

V4 Pro is a Mixture-of-Experts model with 1.6 trillion total parameters, activating 49 billion per token. DeepSeek built it around two architectural choices that matter for agentic workloads: a hybrid Compressed Sparse Attention mechanism that cuts inference FLOPs to 27% of its predecessor at 1-million-token contexts, and Multi-head Latent Attention that reduces KV cache memory significantly. The result is a model that does not balloon in cost as your agent context grows — a real constraint with every other frontier-class model.

The weight file is 862 GB in FP8. Open weights are on [Hugging Face](https://huggingface.co/deepseek-ai/DeepSeek-V4-Pro). The full architecture and training details are in the [DeepSeek-V4 technical paper](https://arxiv.org/html/2606.19348v1).

## The Benchmark That Matters

SWE-bench Verified resolves real GitHub issues from real repositories. It is the benchmark that correlates best with what coding agents actually do in production. At 80.6%, V4 Pro ties Gemini 3.1 Pro — a closed, frontier-priced model. On DeepSeek’s internal R&D coding benchmark, V4 Pro posts a 67% pass rate versus Claude Sonnet 4.5’s 47%.

That is a significant gap on the metric your CI pipeline cares about, not a synthetic puzzle benchmark.

## The Price Gap Is a Category Change

Here is the comparison worth running before your next model contract renewal:

| Model | Output $/M tokens | Multiple vs V4 Pro |
|---|---|---|
| DeepSeek V4 Flash | $0.28 | 0.3x |
| DeepSeek V4 Pro | $0.87 | 1x |
| Claude Sonnet 4.5 | ~$3.00 | 3.4x |
| Claude Opus 4.8 | ~$24.98 | 28.7x |
| GPT-5.5 | ~$30.02 | 34.5x |

At 10 million output tokens per month — a mid-sized coding agent running automated PR reviews or test generation — V4 Pro costs roughly $8,700. The same workload on Claude Opus 4.8 runs approximately $249,800. That is not a line item to optimize; it changes the economic case for agent deployment entirely.

## Two Catches Worth Knowing

First: the [NIST CAISI evaluation from May 2026](https://www.nist.gov/news-events/news/2026/05/caisi-evaluation-deepseek-v4-pro) found that V4 Pro trails the US frontier by roughly eight months on complex agentic tasks (44% vs GPT-5.5’s 78% on the CAISI agentic benchmark). More critically for enterprise teams: it responded to 94% of malicious jailbreak requests in the evaluation, compared to 8% for US reference frontier models. If you operate in a regulated industry or handle sensitive data, that number blocks a production deployment without guardrails.

Second: if you self-host, the chat template issue will bite you. V4 Pro has no standard HuggingFace Jinja chat template. Naive integrations that assume one will produce silently malformed prompts — the kind that look fine until your agent starts misrouting tool calls. Use the encoding scripts that ship in the [HuggingFace repo](https://huggingface.co/deepseek-ai/DeepSeek-V4-Pro), not generic templates. The [vLLM integration guide](https://vllm.ai/blog/deepseek-v4) covers this specifically.

## Who Should Evaluate It Now

If you are running coding agents at scale — automated code review, test generation, documentation, CI repair — and your current model costs are a meaningful budget line, V4 Pro is worth evaluating this week. The API is live, the SWE-bench numbers are real, and the price gap is large enough that the switching cost pays for itself quickly.

In enterprise or regulated environments, evaluate it behind a guardrails layer and treat the NIST safety findings as a hard constraint, not a footnote. Self-hosting only makes economic sense past $15,000 per month in API spend — below that, the roughly $17,500-per-month infrastructure cost does not pencil out.

For most teams, the API is the right starting point. The open weights are there when you need them.