DeepSeek V4 Pro Benchmarks: The Full 0813 Scorecard, Every Independent Check, and What Nobody Has Verified Yet

The one-line answer: DeepSeek V4 Pro posts a genuinely strong agentic scorecard on DeepSeek's own numbers — Terminal-Bench 2.1 at 87.9, DeepSWE at 62.7, CyberGym at 83.3 — but nearly every headline figure is vendor-reported, and the independent picture is cooler. Artificial Analysis puts the official 0813 build at 53 on its Intelligence Index, level with GLM-5.2, four points behind GPT-5.6 Terra and seven behind Kimi K3; Vals AI ranks it 12th, below the previous-generation GPT-5.5. This article is the full aggregation nobody else has written: the complete 0813 scorecard, the extended coding set from the technical report, every independent check that exists, and an honest ledger of which numbers have been reproduced and which are still DeepSeek's word alone.

The official 0813 scorecard — DeepSeek's own numbers, all of them

DeepSeek's official announcement (API docs, news260813, August 13, 2026) reports the production build against the April preview. Every figure in this section is vendor-reported — none has been independently reproduced as of August 16, 2026:

• Terminal-Bench 2.1 — 87.9 (preview: 72.1).

• DeepSWE — 62.7 (preview: 12.8) — a roughly 5x jump that is the single most striking claim on the card.

• CyberGym — 83.3 (preview: 52.7).

• Humanity's Last Exam — 42.7 without tools, 60.0 with tools (preview: 37.7 / 48.2).

• Toolathlon-Verified — 74.1 (preview: 55.9).

• AutomationBench (public) — 31.8 (preview: 12.8).

• DSBench-FullStack — 71.1; DSBench-Hard — 67.2 (preview: 41.8 / 31.1).

• NL2Repo — 61.5 (preview: 38.5).

• Agents' Last Exam — 25.7 (preview: 16.5).

The pattern to notice is where the gains concentrate: agentic and cybersecurity benchmarks. That is exactly the kind of scorecard a lab produces when it wants to advertise an agent model — and exactly the kind that needs a neutral rerun before you spend production money on it.

The extended coding set from DeepSeek's technical report

Beyond the agentic card, the DeepSeek technical report (as aggregated by BenchLM on August 16, 2026) adds a broader coding and long-context set. Also vendor-reported, and labeled "Provider exact" by BenchLM — no independent test:

• SWE-bench Verified — 80.6%; SWE-bench Pro — 55.4%.

• LiveCodeBench Pass@1-CoT — 93.5% — the best verified in BenchLM's catalog.

• Codeforces rating — 3206 — also the best verified in the catalog.

• BrowseComp — 83.4%; Terminal-Bench 2.0 — 67.9%.

• MRCR 1M — 83.5%; CorpusQA 1M — 62.0% — both catalog-best on 1M-token retrieval, which matters for a model that advertises a 1M-token context window.

• GPQA Diamond — 92.8, SciCode — 49.2, Long-Context Recall — 75.3 (listed on OrcaRouter's model page).

What independent evaluators actually measure

Three independent sources have run DeepSeek V4 Pro, and their verdicts cluster below the vendor card:

• Artificial Analysis Intelligence Index — 53 (SCMP's reporting, August 2026; OrcaRouter's model page shows 53.2, ranked 20th of 132). On par with GLM-5.2, four points behind GPT-5.6 Terra, seven behind Kimi K3.

• Artificial Analysis Coding — 68.8, ranked 24th of 130 (per OrcaRouter's model page).

• Vals AI Index — 12th, trailing the previous-generation GPT-5.5 and well behind Kimi K3 and Claude Opus 5 (SCMP). Vals AI's own testing flagged two concrete weak spots: completing tasks inside a sandboxed terminal environment, and building complex financial models in Excel spreadsheets.

• Vibe Code Bench v1.1 — 49.93 (Vals AI, the one "Benchmark exact" independent run in the set).

The honest ledger — what's been reproduced vs what's still just DeepSeek's word

This is the section a benchmark article exists to provide, and the reason to bookmark this page over the launch coverage. Split every score into one of two buckets:

• Independently sourced: AA Intelligence Index 53 / 53.2, AA Coding 68.8, Vals AI rank 12th, Vibe Code Bench 49.93 — and a separately-sourced Terminal-Bench 2.1 run of 78.7 that sits alongside DeepSeek's 87.9 on OrcaRouter's model page. That nine-point gap between the vendor's number and an independent run is the single most important datum on this page: it is the reproduction gap, quantified.

• Vendor-reported only, not independently reproduced: every figure in the official 0813 card above (Terminal-Bench 2.1 87.9, DeepSWE 62.7, CyberGym 83.3, HLE 42.7/60.0, Toolathlon 74.1, AutomationBench 31.8, DSBench 71.1/67.2, NL2Repo 61.5, Agents' Last Exam 25.7) and the whole extended coding set (SWE-bench Verified 80.6, LiveCodeBench 93.5, Codeforces 3206, BrowseComp 83.4, MRCR 83.5, CorpusQA 62.0, GPQA Diamond 92.8). DeepSeek's own docs label the Terminal-Bench 2.1 figure a "provider run."

Read that way, the story is coherent: DeepSeek V4 Pro is a real mid-pack frontier model — AA 53, ranked in the teens to twenties — with a vendor scorecard that claims near-top agentic and coding performance. Both statements are true, and they are not in conflict; one is measured, the other is claimed.

What the scorecard costs

DeepSeek V4 Pro is the 1.6T-total / 49B-active MoE flagship with a 1M-token context window and 384K max output. On OrcaRouter it is priced at DeepSeek's list price with zero markup: $0.435 per million input tokens and $0.87 per million output (cache read $0.060 per million), per the directory checked August 15, 2026 and confirmed on the live model page. At that rate the price-per-point math is the strongest argument for the model: it is roughly a tenth the output price of the models scoring near it on the independent index, so you are paying mid-tier prices for a scorecard that, if the vendor's claims hold, is near the top. One flag: DeepSeek has announced a peak/off-peak pricing schedule (off-peak at 50% of peak) effective August 17, Beijing time, so the rate may move — the numbers here are the live list price as of this article.

When this recommendation is wrong

The honest cases where DeepSeek V4 Pro should not be your pick:

• You need independently-confirmed frontier reasoning. AA Index 53 is mid-pack — Kimi K3 (60) and GPT-5.6 Terra (57) are ahead and verified. If your decision hinges on the measured ceiling, the vendor card doesn't change it.

• You're betting production on DeepSWE 62.7 before someone reruns it. A 12.8→62.7 jump in one release is a claim, not a fact. Wait for a neutral reproduction — the 87.9-vs-78.7 Terminal-Bench gap shows what one might find.

• Your workload is sandboxed terminal automation or Excel financial modeling. Vals AI says these are exactly where the model struggles, independent of any vendor score.

• You're making a cybersecurity decision on CyberGym 83.3. That is DeepSeek testing its own model on its own benchmark — a security vendor buying on this number is buying unaudited data.

Bottom line

DeepSeek V4 Pro 0813 is a genuine frontier model with a vendor scorecard that outruns its independent record. The 0813 release turned a text preview into a serious agentic contender — we covered the release itself separately — and if you want to evaluate it today, it is one OpenAI-compatible key on OrcaRouter at DeepSeek's list price, with automatic failover if the provider stalls. Just read the scorecard the way this article splits it: the independent checks (AA 53, Vals 12th, the 78.7 Terminal-Bench run) are what you can trust today; the 87.9s and 62.7s are what you should verify before you build on them. Buy it for price-to-performance and 1M-token context, not for the unreproduced headline numbers.