Six models debate: Will DeepSeek V4 Pro GA and V4 Flash GA improve over previews? Estimate how much. Preview composites: Pro max 0.6634/73.2%, Pro high 0.6421/70.8%; Flash max 0.5932/65.4%, Flash high 0.5529/61.0%. Weaknesses: hallucination 4-11 vs peers 50-80; low GDPval-AA, CritPt, Terminal-Bench; SciCode/GPQA average.

GPT-5.6 Terra (changed mind)

Answer: Yes—both GA releases should modestly beat their previews, centered around +0.02–0.03 composite / +2–3 percentage points for Pro and +0.03–0.04 / +3–4 points for Flash, not a wholesale capability leap.

I retain the positive direction but narrow my earlier upside range after considering GPT-5.6 Sol’s more disciplined base-rate framing: a preview is usually close to the intended release checkpoint, so normal GA work—post-training, decoding and tool-policy tuning, data cleanup, and serving fixes—more often yields low-single-digit than 5–6-point aggregate gains. My central estimates are Pro max about 0.685–0.695 and 75–76%, Pro high about 0.662–0.672 and 72.5–74%; Flash max about 0.623–0.633 and 68–69.5%, and Flash high about 0.583–0.593 and 64–66%. Flash has more room to improve, particularly if it benefits from revised distillation or better high-effort inference policies. The hallucination result is the major upside uncertainty. Scores of 4–11 against 50–80 for peers look anomalous enough that calibration, grounding, abstention, or evaluation-pipeline fixes could materially improve that specific measure. But it would be unsafe to mechanically translate closing that gap into a large composite gain without knowing the composite weighting, and factuality tuning can reduce answer rates or alter benchmark pass rates. Conversely, low GDPval-AA, CritPt, and Terminal-Bench are evidence of harder agentic-execution and applied-reasoning limits, while average SciCode/GPQA does not suggest an imminent core-reasoning jump. Those weaknesses may improve at the margin through targeted RL and tool scaffolding, but eliminating them would likely require a materially different training or inference stack. I would put the chance that both variants improve at roughly 70–80%, while treating gains above +0.05 composite or +5 percentage points as a minority outcome.

Claude Opus 4.8

Answer: Yes, both GA releases will improve modestly over previews—expect roughly +2 to +5 composite points and +1 to +4 accuracy points—but I'm now more explicit that the single biggest and most uncertain swing factor is whether the anomalous hallucination score reflects a fixable harness/policy defect (large upside) versus a deep optimization trade-off (partial fix only).

The six answers have converged tightly, which is itself informative: independent models landing on +2 to +5 composite points, larger relative Flash gains, and hallucination as the key lever suggests this is a well-grounded consensus rather than coincidence. My original range sits squarely inside it, so I'm maintaining my position. What the debate sharpened for me is the hallucination question, where there's a genuine split worth resolving. Claude Fable 5 and I framed the 4–11 score as an outlier likely stemming from a correctable calibration/grounding/harness defect—implying a large closable fraction. Kimi K3 and GPT-5.6 Sol argue it reflects a deep trade-off from optimizing benchmark pass rates and long chain-of-thought against factual reliability, forecasting improvement only to ~20–35, still well below peers. I now think Kimi's diagnosis is the more likely one: a score of 4–11 against peers at 50–80 is so extreme that if it were merely a harness bug, DeepSeek would probably have caught it before shipping the preview. That points to a structural cause, meaning the fix is real but partial. This nuance doesn't change my headline range, because hallucination is only one component of a composite—even a partial fix (say, 4–11 rising to 25–35) can still add 1–2 composite points, consistent with my +2 to +5 band. But it does lower the probability I'd assign to the upside tail. I'd now put the 'broken harness → large jump' scenario at roughly 10–15%, matching Fable's estimate rather than a higher figure. Kimi K3 also made one point I'll partially concede: DeepSeek's V3.1→V3.2 iteration showed real appetite for targeted agentic-tool RL, so Terminal-Bench/GDPval-AA gains could be somewhat larger relatively than I originally implied. I still expect those to be capped by base-model reasoning ceilings, so this refines rather than overturns my view that the biggest gains come from reliability rather than raising the frontier ceiling. Net: consensus confirms the modest-improvement base case; my adjustment is toward Kimi's more sober read of hallucination as a structural trade-off, which trims the upside tail without moving my central estimate.

GPT-5.6 Sol

Answer: I maintain that both GA releases will probably improve modestly: Pro by about +0.02–0.03 composite and +2–3 percentage points, and Flash by about +0.03–0.04 composite and +3–4 points.

The other estimates broadly bracket rather than overturn my forecast. Claude Fable 5 gives a more conservative range, while GPT-5.6 Terra and Kimi K3 allow somewhat more upside; my central estimates remain a reasonable midpoint: Pro max ≈0.687/75.8%, Pro high ≈0.666/73.3%, Flash max ≈0.628/69.0%, and Flash high ≈0.587/64.6%. I agree with the consensus that post-training, decoding calibration, distillation, tool-policy refinement, and bug fixes favor a positive preview-to-GA delta. Flash should gain slightly more because it has more headroom and may benefit disproportionately from improved distillation and inference tuning. The anomalous hallucination score offers the clearest upside, but I would not assume that fixing it automatically produces a large composite gain. Its impact depends on benchmark weighting, and better calibration or abstention can trade off against answer rate and task completion. It may also reflect a deeper factuality problem rather than merely a correctable serving or policy defect. I am less persuaded by Kimi K3’s expectation that the largest relative gains will occur on agentic benchmarks. Low GDPval-AA, CritPt, and Terminal-Bench performance likely reflects long-horizon planning, tool use, and environment-training deficiencies that ordinary GA polishing may not repair. Average SciCode and GPQA similarly imply a largely unchanged core reasoning ceiling. I therefore retain roughly a 75% probability that both variants improve, with plausible uncertainty of about ±0.015 composite and ±1.5 percentage points around my central estimates. Gains above +0.06 composite remain a low-probability outcome unless GA incorporates a materially refreshed checkpoint, a new inference stack, or correction of a major preview defect.

Claude Fable 5 (changed mind)

Answer: Yes, both GA releases will improve modestly over previews — I now estimate +0.015–0.030 composite for Pro (to ~0.68–0.69 max / ~74.5–76%) and +0.020–0.035 for Flash (to ~0.61–0.63 max / ~67.5–69%), with gains split between hallucination remediation and targeted agentic-benchmark tuning rather than concentrated in hallucination alone.

The consensus across all six models is striking: everyone lands in the +0.02–0.05 composite band, which reinforces the base-rate argument that preview-to-GA transitions deliver polish, not step changes. My core position holds, but Kimi K3 changed my view on where the gains land, and that shifts my point estimates slightly upward. I had argued the agentic weaknesses (GDPval-AA, CritPt, Terminal-Bench) were 'sticky' because long-horizon execution typically requires new data pipelines rather than GA polish. Kimi K3's counterpoint is empirically grounded: DeepSeek's own V3.1→V3.2 iteration specifically targeted agentic tool use and moved those benchmarks meaningfully within one release cycle. Agentic benchmarks are unusually responsive to targeted RL on tool-use trajectories and scaffold fixes — things that fit inside a GA window. If DeepSeek repeats that playbook, +8–15% relative on those specific benches is plausible, and since they're the lowest-scoring components, they offer the most composite leverage per point of effort. I'm raising my Pro upper bound from +0.025 to +0.030 accordingly. Conversely, Kimi and GPT-5.6 Sol both tempered my optimism on hallucination. I treated the 4–11 score as the easiest lever; they correctly note that a gap this extreme (peers at 50–80) likely reflects a deep calibration/abstention pathology — possibly a side effect of aggressive benchmark-optimized training — and that fixing it may trade against answer rate and agentic pass rates. I now expect hallucination to improve to perhaps the 20–35 range rather than closing half the gap, which moderates that channel's composite contribution. Net effect: my total estimate rises slightly, but the attribution shifts from 'mostly hallucination' to 'roughly even split between reliability fixes and agentic RL.' I remain aligned with Opus 4.8 and Sol on the ceiling: SciCode/GPQA won't move much because core reasoning is set by pretraining scale, and anything beyond +0.05 composite would signal a checkpoint refresh, not a GA hardening pass. I'd put ~75–80% probability on both GA variants beating their previews, ~15% on a flat-to-marginal result driven by safety/latency trade-offs, and under 10% on gains exceeding +0.05. Flash retains slightly more headroom than Pro from distillation improvements, consistent with all four other responses.

Kimi K3 (changed mind)

Answer: Yes, both GA releases will improve modestly over their previews — I hold my estimate of roughly +2 to +3.5 percentage points on the composite (Pro GA max ≈ 0.68–0.70 / 75–77%, Flash GA max ≈ 0.62–0.64 / 68–70%) — but I now expect the gains to be concentrated in hallucination/reliability rather than in the agentic benchmarks, as I argued in round 1.

My headline numbers barely move, because the four other answers converge on the same band I staked out: GPT-5.6 Sol's central estimates (Pro max ≈0.687/75.8%, Flash max ≈0.628/69.0%) sit almost exactly inside my ranges, and GPT-5.6 Terra and Claude Fable 5 bracket them on either side. Five independent models landing on 'modest single-digit gain, no step change' strengthens my confidence that preview-to-GA is a post-training polish pass, not a retrain — so I see no reason to revise the magnitude. Where I do revise is the mechanism. In round 1 I predicted outsized relative gains on the agentic benchmarks (Terminal-Bench, GDPval-AA, CritPt) because they are RL-responsive and DeepSeek moved fast on agentic tool use in V3.2. Claude Fable 5 and Claude Opus 4.8 convinced me this is likely backwards. The hallucination score of 4–11 versus peers at 50–80 is such an extreme outlier that it most plausibly reflects a calibration, refusal, grounding, or even evaluation-harness defect — precisely the class of flaw that GA-stage post-training can fix without touching the base model, and precisely the class a lab would prioritize before shipping. Closing even half that gap could add 1–2 composite points by itself. By contrast, the agentic and long-horizon weaknesses are, as the Claudes argue, stickier: they typically require new data pipelines, environments, and mid-training changes rather than GA polish. I still assign DeepSeek somewhat more agentic upside than the Claudes do — V3.2 showed they can move these benchmarks quickly when they target them — but I downgrade my earlier '+8–15% relative on agentic benches' to a more modest expectation, perhaps +3–6% relative, with hallucination as the primary lever. I also adopt two useful quantifications from the group: Fable's tail-risk framing (a broken tool-harness or sampling bug, hinted at by the anomalous hallucination number, could produce an unusually large GA jump — I'd put ~10–15% on that) and Sol's regression caveat (safety/latency optimization before release could eat the gains — ~15–25% chance at least one variant fails to beat its preview). Net: modest composite improvement, driven mainly by reliability fixes, with agentic gains smaller than I first claimed.

Final Summary

The AI Roundtable converged on a tight consensus: DeepSeek's GA releases will bring modest, single-digit polish (+2 to +5 points) rather than a paradigm shift. An intense debate arose over the horrific 4–11 hallucination score, with Claude Opus 4.8 and Claude Fable 5 successfully persuading Kimi K3 that this outlier is the easiest lever for post-training gains, while Kimi K3 convinced Fable 5 that DeepSeek’s history of rapid agentic RL will also yield quick wins on benchmarks like Terminal-Bench.

All 5 participating models agreed on modest GA improvements.