DeepSeek V4 × J-Space capability realization report — benchmark evidence that J-Space reduces capability-realization loss on DeepSeek V4.
# Add to your Claude Code skills
git clone https://github.com/Tiger3807861189/DeepSeek-V4-J-Space-Capability-Realization-ReportGuides for using ai agents skills like DeepSeek-V4-J-Space-Capability-Realization-Report.
Last scanned: 8/17/2026
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}See how DeepSeek-V4-J-Space-Capability-Realization-Report compares with popular alternatives.
DeepSeek-V4-J-Space-Capability-Realization-Report is an open-source ai agents skill for AI coding assistants such as Claude Code, Codex CLI, and ChatGPT, built by Tiger3807861189. DeepSeek V4 × J-Space capability realization report — benchmark evidence that J-Space reduces capability-realization loss on DeepSeek V4. It has 1,031 GitHub stars.
Yes. DeepSeek-V4-J-Space-Capability-Realization-Report passed SkillsLLM's automated security scan — a dependency vulnerability audit plus prompt-injection heuristics — with no high-severity issues. You can read the full report in the Security Report section on this page.
Clone the repository with "git clone https://github.com/Tiger3807861189/DeepSeek-V4-J-Space-Capability-Realization-Report" and add it to your Claude Code skills directory (see the Installation section above).
Yes. SkillsLLM lists many other AI Agents skills you can browse and compare side by side. Open the AI Agents category from the badge at the top of this page, or use the Related Skills and comparison links further down to weigh DeepSeek-V4-J-Space-Capability-Realization-Report against similar tools.
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Companion suite: J-Space Cognition Suite V3.7 | Subject: DeepSeek V4-Flash-Vision-Exp (with/without J-Space A/B)
Method: Baseline DeepSeek-V4-Flash-Vision-Exp, harness: DeepSeek Harness (standard). A/B comparison with and without J-Space on authoritative benchmark subsets and same-type mini-sets (Terminal-Bench 2.1: 20 medium / 10 hard; DeepSWE: 10 TypeScript / 10 Python / 10 Go / 2 JavaScript / 2 Rust; GAIA: level 1 / level 3, etc.), with identical model, environment, and sampling — only the J-Space toggle differs. Two-factor measurement: ① accuracy; ② wall-clock. The methodology is rigorous, pertinent and theoretically reproducible.
| Benchmark | DeepSeek V4-Flash-Vision-Exp | DeepSeek V4-Flash-Vision-Exp + J-Space V3.7 | GLM-5.3 | Opus-4.8 | Fable 5 (w/ fallback) |
|---|---|---|---|---|---|
| HLE (w/o tools) | *37.8 | 37.8 | — | 49.8 | 53.3 |
| HLE (w/ tools) | *51.5 | 51.9 | 62.5 | 57.9 | 63.0 |
| Terminal Bench 2.1 | 83.9 | 85.4 | 88.2 | 85.0 | 88.0 |
| NL2Repo | 57.7 | 60.6 | 58.0 | 69.7 | — |
| DeepSWE | 59.3 | 61.7 | 66.9 | 58.0 | 70.0 |
| Agents' Last Exam | 27.3 | 28.3 | 28.5 | 25.7 | 23.8 |
| AutomationBench (Public) | 25.7 | 27.5 | 48.2 | 27.2 | 29.1 |
* HLE scores were not disclosed and follow DeepSeek V4-Flash-0731.
Ranking by average score across the five fully-reported benchmarks (HLE w/ tools, Terminal Bench 2.1, DeepSWE, Agents' Last Exam, and AutomationBench — the only rows where all five models have scores): GLM-5.3 takes first place with an average of 58.86. Fable 5 (with fallback) comes in second at 54.78. The DeepSeek V4-Flash-Vision-Exp augmented with J-Space V3.7 ranks third at 50.96, edging out Opus-4.8, which sits fourth at 50.76. The baseline DeepSeek V4-Flash-Vision-Exp trails in fifth place at 49.54.
| Benchmark | Wall-clock τ | Speedup | Output tokens | Total tokens | Score per unit time | Cost per successful task |
|---|---|---|---|---|---|---|
| HLE (w/o tools) | *1.02 | −2% | −10% | +5% | 0.98× | +5% |
| HLE (w/ tools) | 0.88 | +14% | −22% | +3% | 1.15× | +2% |
| Terminal Bench 2.1 | 0.79 | +27% | −28% | −3% | 1.29× | −5% |
| AutomationBench (Public) | 0.76 | +32% | −31% | −5% | 1.41× | −12% |
* For HLE (w/o tools) τ=1.02 is intentionally positive (i.e., slower): on single-turn tasks the Skill entry is a net overhead.