On this page
On the ARC Prize official results page for DeepSeek V4.1 Flash 1017, the page header reads DeepSeek · Oct 17, 2026 · 5 harness configurations. Under that banner, the same model at the same max reasoning setting lands 0.2% on ARC-AGI-3 with the Standard harness and 5.8% with the Provider Adapter harness. That gap is not a quiet second weight drop and not a new model release. It is the evaluation run framing—what the harness is allowed to keep between steps—showing up as two very different headline percentages on one Verified scores row.
Related reading: How DeepSeek V4.1 Flash 1017 Ranks on Code Arena, Vals AI and Artificial Analysis Benchmarks, Same Model, Different Coding Harness: Pass Rate Barely Moves, the Bill Does, Microsoft Copilot Harness Lands in Foundry Agent Service Preview, and DeepSeek V4.1 Flash Is Now Available in Cursor. The first piece covers the open-weight / API / other-leaderboard background for this checkpoint. The second is about coding harness cost and pass rate—useful for contrast, not the same topic as ARC evaluation harnesses. The last two are neighboring product-harness and Cursor availability notes. None of them replaces reading the ARC-AGI-3 harness gap on its own terms.
Numbers on the ARC Prize results page
The page-level Semi-Private highlights are 94.5% on ARC-AGI-1 at high reasoning and 78.1% on ARC-AGI-2 at max reasoning. For ARC-AGI-3 (v3), the same page states 0.2% with Standard and 5.8% with Provider Adapter, both at max reasoning. The Verified scores table Max row lines up the same way: ARC-AGI-1 93.5%, ARC-AGI-2 78.1%, ARC-AGI-3 Standard 0.2%, ARC-AGI-3 Provider Adapter 5.8%. High / Low / None columns for the two ARC-AGI-3 harness cells are listed as —.
There is no separate “evaluation publish date” field called out on that results page. The visible date that readers will see first is the model-day label Oct 17, 2026 (page clock in local Asia/Shanghai reading of the same calendar day). All percentages below are ARC Prize self-reports. We haven't retested them on DigIntoAI hardware; they are not an independent DigIntoAI lab run.
Two harnesses, in plain language
Standard harness, as the ARC Prize page defines it, lets the model carry forward notes it chooses to keep throughout an environment. A plain picture is a scratchpad the run is allowed to maintain: observations, partial plans, reminders the model elected to write down and take into later steps inside that environment.
Provider Adapter harness, again in the page’s own wording, preserves opaque / internal reasoning state between requests and applies compaction on longer conversations so prior work can be reused. Instead of only the notes the model deliberately kept, the adapter path keeps more of the provider-side reasoning residue across turns and compresses long context so earlier progress is not thrown away as the dialogue grows.
Same checkpoint. Same max reasoning knob. Different rules for what survives between steps. The headline ARC-AGI-3 pair—0.2% vs 5.8%—is the aggregate expression of that difference on the Verified scores table.
One vocabulary warning for DigIntoAI readers: on this site, “harness” often means a coding agent wrapper—token bill, pass-rate tables, install guides. Same Model, Different Coding Harness: Pass Rate Barely Moves, the Bill Does is that coding-cost story. The ARC page’s Standard / Provider Adapter labels are evaluation harnesses for how ARC-AGI-3 environments are driven. Same English word; different job. Mixing them up turns a leaderboard-reading tip into the wrong product story.
Environment snapshots (still official, still unretested)
The ARC-AGI-3 Public Demo tables list 25 environments per harness. A few Max-row pairs show how uneven the gap can be—without copying the whole grid:
| Environment | Standard Max | Provider Adapter Max |
|---|---|---|
| FT09 | 3.4% | 91.4% |
| WA30 | 0.0% | 22.2% |
| LP85 | 2.8% | 24.5% |
| AR25 | 7.4% | 8.8% |
FT09 is the dramatic case: Standard stays near the floor while Provider Adapter jumps into the nineties. WA30 and LP85 move from near-zero / low-single-digits into the twenties. AR25 barely budges (7.4% → 8.8%)—a useful reminder that the adapter path does not inflate every environment the same way. Aggregate 0.2% vs 5.8% is an average story over those environments, not a promise that every task doubles or that day-to-day coding output jumped by the same ratio.
None of these environment figures were re-run on DigIntoAI hardware. They are read off the ARC Prize results page as published.
Same checkpoint — not a new release, not a coding uplift
- Not a new DeepSeek model. The page is labeled DeepSeek V4.1 Flash 1017. The harness comparison sits on that checkpoint’s results sheet.
- Not a new weight release dressed up as a leaderboard update. The variable called out next to the ARC-AGI-3 pair is harness configuration, under a header that already lists 5 harness configurations.
- Not proof of a general capability leap. Moving from Standard notes to Provider Adapter state-plus-compaction changes the evaluation contract. That can raise ARC-AGI-3 numbers without licensing a claim that everyday programming productivity rose by the same factor.
- Not an independent DigIntoAI scoreboard. Verified scores, Semi-Private highlights, and environment Max cells are official self-reports. We haven't retested.
Open weights, the shared deepseek-flash API name, and how this checkpoint shows up on Code Arena / Vals AI / Artificial Analysis are covered in the earlier piece How DeepSeek V4.1 Flash 1017 Ranks on Code Arena, Vals AI and Artificial Analysis Benchmarks. That article is background, not a substitute for the ARC harness reading.
A short checklist for reading the ARC page
- Model name first—but not alone. “DeepSeek V4.1 Flash 1017” identifies the checkpoint; it does not tell which harness row or reasoning column produced a quoted percentage.
- Harness next. On ARC-AGI-3, Standard (kept notes) and Provider Adapter (opaque state + compaction) are different evaluation contracts. Quoting one number without the harness label is incomplete.
- Reasoning level. The ARC-AGI-3 pair above is at max. Semi-Private peaks on the same page use high for v1 (94.5%) and max for v2 (78.1%). Mixing levels across citations creates fake gaps.
- Which ARC generation. ARC-AGI-1 Max 93.5% and ARC-AGI-2 Max 78.1% sit on the same Verified scores row as the ARC-AGI-3 harness pair. They answer different benchmarks; stacking them as one “DeepSeek score” erases that.
- Aggregate vs environment. Headline 0.2% / 5.8% can hide pairs like FT09 (3.4% / 91.4%) and near-flat pairs like AR25 (7.4% / 8.8%). When a claim hinges on one environment, that cell is the evidence.
- Self-report stamp. Every figure stays ARC Prize-published until a separate retest exists. We haven't retested.
Bottom line
DeepSeek V4.1 Flash 1017 on the ARC Prize official results page (label Oct 17, 2026, 5 harness configurations) posts ARC-AGI-3 0.2% under Standard and 5.8% under Provider Adapter at the same max reasoning setting. Standard keeps model-chosen notes; Provider Adapter keeps opaque reasoning state across requests and compacts long conversations. That is an evaluation-harness difference on one checkpoint—not a new weight drop, not a new model name, and not a license to invent daily coding uplift from the percentage gap. The model name, the harness, and the reasoning column belong together; coding-harness cost articles sit on a separate shelf. Figures above are ARC Prize self-reports; we haven't retested. Field notes can go through the site-owner contact in the footer.