DeepSeek V4 Pro 0813 Reality Check: 96.4% SWE-bench Verified, but Terminal-Bench Swings 87.9% to 54.68%

Why DeepSeek V4 Pro is trending again

DeepSeek-V4-Pro-0813 is not a September 7 model launch: DeepSeek moved the checkpoint to general availability on August 13, 2026. What is newly useful is the evidence split now visible across current leaderboards and evaluator reports. Some coding boards surface the model near the top on SWE-bench Verified, while DeepSeek's own Terminal-Bench 2.1 result is dramatically higher than a separately run evaluator result for the same checkpoint.

That makes V4 Pro 0813 a useful case study in a broader rule for agent benchmarks: the model name is only part of the evaluated system. The harness, tools, prompt, reasoning effort, timeout, retry policy, context handling and stopping rules can materially change the score.

Exact identity and availability

The exact checkpoint is DeepSeek-V4-Pro-0813. DeepSeek's current API documentation says the rolling API model ID deepseek-v4-pro serves this 0813 version. The official Hugging Face repository is MIT licensed and describes the model as the general-availability successor to the V4 Pro preview, with a DSpark speculative-decoding module attached.

DeepSeek's current API table lists a 1 million-token context window, a 384K maximum output, tool calling, JSON output, native Responses API support and an Anthropic-compatible API. The current documented concurrency limit for V4 Pro is 500.

The official model card displays roughly 1.7T parameters in Hugging Face metadata, while DeepSeek's V4 family materials describe the architecture as about 1.6T total parameters with 49B active per token. Those two presentation conventions should not be treated as two different checkpoints.

Vendor result: 87.9 on Terminal-Bench 2.1

DeepSeek's official model card reports 87.9 on Terminal-Bench 2.1 for V4 Pro 0813, up from 72.1 for the preview checkpoint. The same table reports 62.7 on DeepSWE, 83.3 on CyberGym, 61.5 on NL2Repo, 74.1 on Toolathlon-Verified and 31.8 on AutomationBench Public.

The footnote matters. DeepSeek says public code-agent tasks were evaluated using the minimal mode of DeepSeek Harness, at max reasoning effort, temperature = 1.0 and top_p = 0.95.

So the defensible statement is not “the bare model scores 87.9.” It is that DeepSeek V4 Pro 0813, under DeepSeek's selected harness and max-effort configuration, scored 87.9 in the vendor evaluation.

Independent evaluator result: 54.68 on Terminal-Bench 2.1

Vals AI publicly reported a much lower 54.68% across three full Terminal-Bench 2.1 trials, with 28.89% on hard tasks, for DeepSeek V4 Pro 0813. The evaluator's public post says the run used max reasoning effort and provider-default settings apart from temperature 1.

That is a 33.22 percentage-point gap from DeepSeek's 87.9 result.

It is tempting to call one score right and the other wrong. The available evidence does not justify that. The two runs are not documented as bit-for-bit replications of the same agent system. A different harness can change tool exposure, system prompt, retries, time budget, state management and termination behavior.

The safe conclusion is narrower and more important: Terminal-Bench performance for this checkpoint is highly harness-dependent in the public evidence we can inspect.

SWE-bench Verified tells a different story

Current coding leaderboards attribute a 96.4% SWE-bench Verified result to DeepSeek V4 Pro 0813, with the result traced to Vals AI's shared evaluation setup. Multiple public summaries of that run describe a common mini-swe-agent/bash-style harness over the 500 human-validated SWE-bench Verified tasks and report an uncertainty around ±0.83 points.

This is not DeepSeek's own number: the official 0813 model card does not publish a SWE-bench Verified row. That distinction is useful because it prevents a common attribution error in which a third-party result is silently converted into a vendor claim.

A very high score on SWE-bench Verified also does not resolve the Terminal-Bench disagreement. The benchmarks test different things. SWE-bench Verified is dominated by repository issue resolution with test-based acceptance. Terminal-Bench evaluates broader terminal-agent behavior over interactive tasks. Strong performance on one should not be used to impute a score on the other.

SWE-bench Pro is separate, and the exact 0813 result remains unverified here

SWE-bench Verified and SWE-bench Pro must be reported separately. In this bounded September 7 review, I did not find an exact, version-pinned DeepSeek-V4-Pro-0813 SWE-bench Pro result in DeepSeek's official 0813 model card, the current DeepSeek API documentation, the Vals public post reviewed for this article or Artificial Analysis' current model page.

Some third-party comparison pages quote Pro-family scores, but without a sufficiently clear 0813 checkpoint, split, harness, task count and run date, those numbers should not be silently attached to this exact model.

The correct value for this article is therefore unknown, not an inferred score.

Artificial Analysis v4.2 is a third, different measurement

Artificial Analysis' current page for DeepSeek V4 Pro 0813, Reasoning, Max Effort reports an Intelligence Index v4.2 score of 42, about 70.6 output tokens per second, a $0.33 cost per Intelligence Index task, and 140M output tokens consumed across its evaluation.

That current 42 should not be mixed with older Artificial Analysis scores for the April preview or with earlier index versions. Artificial Analysis v4.2 combines ten evaluation families, including agent, coding, scientific-reasoning and general capability tasks under its own weighting and harnesses.

Its 42 is therefore neither a SWE-bench percentage nor a Terminal-Bench score. It answers a different question: how the model performs on Artificial Analysis' current composite suite under their standardized evaluation configuration.

Current first-party pricing

DeepSeek's current API pricing is time-dependent. For deepseek-v4-pro, the official table lists, per 1 million tokens:

• Off-peak: $0.022 cache-hit input, $0.66 cache-miss input, $1.98 output.
• Peak: $0.044 cache-hit input, $1.32 cache-miss input, $3.96 output.

Peak windows are 01:00–04:00 UTC and 06:00–10:00 UTC, Monday through Friday; other times are off-peak. DeepSeek says these rates took effect on August 16 and reserves the right to change product pricing.

Benchmark “cost per task” is not the same as API list price. It depends on token usage, cache behavior, retries, reasoning length and the harness. A cheap token rate can still produce a costly task if a system consumes substantially more tokens or repeatedly retries.

Latency and throughput

DeepSeek does not publish a single guaranteed end-to-end latency figure for V4 Pro 0813. Artificial Analysis currently measures roughly 70.6 output tokens per second on its tested route for the max-effort configuration. That is an evaluator measurement, not a service-level guarantee.

Latency for an agent workflow also includes thinking time, tool execution, network calls, retries and environment response time. Output tokens per second should therefore not be presented as “time to finish a coding task.”

Why the harness can move an agent score so much

Agent benchmarks differ from static question answering because the model repeatedly observes, acts and corrects itself. Small harness changes can compound over dozens or hundreds of steps.

A harness can affect whether the system finds the right file, whether failed commands are retried, how tool errors are phrased, whether long histories are compressed, whether the model receives explicit verification instructions and when the run is stopped. Reasoning-effort controls can further change both quality and cost.

For DeepSeek V4 Pro 0813, the 87.9-versus-54.68 Terminal-Bench split is a concrete reminder that an agent leaderboard row should ideally name model + checkpoint + harness + tools + reasoning setting + task count + retries + time/token budget + date.

Public feedback is mixed and anecdotal

DeepSeek's official August 13 X launch post announced the V4 Pro general-availability release, three reasoning-effort levels and native Responses API support. A separate official X post the same day announced the peak/off-peak pricing change. These posts are first-party launch communication, not independent validation.

Public Reddit discussion around August 13–14 was mixed. One highly upvoted r/DeepSeek post claimed early production behavior looked worse than expected and speculated about a deployment rollback; other commenters reported that behavior improved later. Another small developer test reported two Pro failures on one demo that Flash completed. These reports are useful as incident signals, but they are self-selected anecdotes with tiny samples, uncontrolled routing and no pinned evaluation harness.

A bounded search did not surface a stable, attributable X post from an independent evaluator publishing a controlled same-checkpoint Terminal-Bench or SWE-bench rerun with raw trajectories. No claim of broad X consensus is justified.

Practical tradeoff

The evidence profile is not “96.4 means best coding model” and it is not “54.68 means the vendor benchmark is invalid.”

The stronger reading is:

DeepSeek V4 Pro 0813 shows extremely strong performance in a shared, bounded repository-repair evaluation, while its Terminal-Bench outcome varies dramatically across agent systems. The checkpoint is open weight, has a 1M context window and comparatively low first-party token prices, but production teams should test it inside the exact harness they plan to deploy.

For well-scoped engineering tasks with clear tests, the SWE-bench Verified evidence is encouraging. For long-running autonomous terminal work, the public score spread says the orchestration layer must be benchmarked alongside the model.

What evidence would change this assessment

The most useful next evidence would be a public, version-pinned DeepSeek submission to an official Terminal-Bench leaderboard with the full harness, prompt, retry and budget configuration; a controlled independent replication of DeepSeek's 87.9 setup; an exact V4-Pro-0813 SWE-bench Pro run with split and task count disclosed; and cost/latency measurements from the same harness used for capability scoring.

Until then, the fairest comparison is a benchmark profile with provenance, not a single universal rank.