DeepSeek-V4-Pro-0813: MindStudio's Eight-Task Coding and Agent Hands-on Comparison

One-sentence takeaway

MindStudio's public eight-task hands-on test gave V4-Pro-0813 a score of 61/80 (76.25%), showing strengths in frontend work, planning, mathematics, and long-horizon agents, while SVG work, game polish, and overthinking simple tasks remain boundaries.

Use cases

• Tasks it suits: Frontend generation, complex planning, long-horizon data-generation/fine-tuning/local-UI tasks, and agent work that requires clarifying ambiguities first.

• Tasks it does not suit: One-line fixes, or situations where the model is expected to always answer briefly and avoid overhauling code; the article observed that Pro can overthink and over-engineer.

• Applicable model version: DeepSeek-V4-Pro-0813 GA; the article explicitly compares it with the April preview.

• Applicable client, agent, or API: The article does not disclose a standardized client; the results come from the author's coding/reasoning benchmark and hands-on use.

• Recommended reasoning tier and parameters: Not disclosed; in an actual integration, the official low/high/max settings can be retested, but the effort setting cannot be inferred from the article's score.

Test environment

• Model/version: V4-Pro-0813; compared with V4 preview, V4 Flash, Kimi K3, Opus 5, Fable 5, Muse Spark 1.2, GLM 5.2, and others (using the names current in the article at the time).

• Inputs/task types: Eight tasks covering a multi-elevator logic simulation, 3D interaction, a folding-table animation, an SVG panda, a bow-and-arrow game, permutation mathematics, long-horizon autonomous data generation/fine-tuning/local Web UI work, and a 3D dual-time-zone watch.

• Evaluation method: Each task received 0–10 points. The article discloses the task types, per-task scores, and total score, but not the complete prompts, model-call parameters, or number of repetitions.

Input/configuration

Temperature, thinking effort, maximum output, tool list, system prompt, random seed, and number of runs were not disclosed. The article also says that community specs list a 1M context, but this has not been confirmed by DeepSeek and cannot be treated as the configuration for this evaluation.

Results data

Official/aggregate leaderboard scores (as reported in the article): Terminal Bench 2.1 87.9; Cyberjim 83.3; Automation Bench 31.8; HLE without tools 42.7, with tools 60. The article does not provide complete harnesses for these projects, so this note records them separately from the eight-task hands-on test.

Conclusions

The eight-task results support including V4-Pro-0813 among candidates for complex frontend and long-horizon agent work; its total score should not obscure its limitations in fine-grained SVG output, edge-case handling, and efficiency on simple tasks. The choice between Pro and Flash should be A/B tested according to “planning/complex tasks vs. everyday execution/speed,” rather than defaulting to Pro for everything.

Limitations and reproduction steps

• Limitations: The article provides no original prompt for each task, tool trace, model configuration, number of repetitions, or blind evaluators; “independent” can only be understood as the author's hands-on test in a non-official article, not as a fully reproducible public dataset.

• Reproduction steps: Recreate the eight task categories and publish the complete inputs; fix the model snapshot, effort, tools, context, and time limit; run each task at least three times; use the same 0–10 rubric for blind evaluation of functionality, visual quality, edge-case handling, and the amount of manual editing; run V4-Flash, Claude, Kimi, and others in the same harness.

• Cost tracking: Record token costs under the official peak and off-peak prices at the same time, so quality advantages are not conflated with price advantages in a single score.

Original evidence and data

The MindStudio article gives per-task scores of 6/8/9/5/6/10/10/7, totaling 61/80, and reports 24.8% for the preview; the body also records firsthand observations of overthinking, over-engineering, frontend generation, planning, and ambiguity-clarification issues. This note does not treat the community rumor of a 1M context as a confirmed specification.

Applicability boundaries

• The sample contains only eight tasks, so task selection and subjectivity in scoring can significantly affect the total.

• The HLE, Terminal, Cyberjim, and other figures in the article are not from the same experiment as the eight-task hands-on test and cannot be combined into an overall ranking.

• The long-horizon task described as involving “no human intervention” still requires reviewing the run logs; the article does not disclose the complete trace.

Source excerpt or observation (short compliant quotation only)

The article's summary of the observed behavior is “overthinks simple problems” and “overengineering”; these two observations are more suitable than the total score for production acceptance tests.