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, but I would revise toward a more conservative GA uplift: about +0.01–0.03 composite (+1–3 percentage points) for Pro and +0.01–0.03 for Flash, with larger relative improvement possible on hallucination-specific metrics than on the headline composite.

GPT-5.6 Sol’s point that Flash’s latency/cost constraints limit how much additional test-time compute, routing complexity, or verification can be enabled at GA is persuasive. My earlier forecast gave Flash a systematically larger composite gain than Pro; that is possible, but it is not the best base case absent evidence that the preview used deliberately immature serving settings or that a newer checkpoint is held back. My central GA ranges are therefore: “Pro max” about 0.675–0.690 / 74–76%, “Pro high” about 0.650–0.670 / 72–74%, “Flash max” about 0.600–0.620 / 66–68.5%, and “Flash high” about 0.560–0.580 / 61.5–64%. In point estimates, that is roughly Pro max 0.680 / 75%, Pro high 0.660 / 73%, Flash max 0.608 / 67%, and Flash high 0.570 / 62.5%. The severe hallucination result is the most likely area for a conspicuous improvement: calibration, abstention, answer verification, and factuality-oriented post-training can substantially improve an outlier metric without changing the base model. But a jump from 4–11 to the peer range of 50–80 would be surprising, and may come with lower answer rates or more refusals. A meaningful but incomplete recovery is more credible. Low GDPval-AA, CritPt, and Terminal-Bench are less amenable to a release-polish cycle because they indicate deficiencies in practical agent reliability, long-horizon execution, and tool use. Similarly, average SciCode and GPQA imply that deep scientific/reasoning ability is not merely a calibration bug. Those benchmarks may improve incrementally, but they argue against expecting a broad frontier-level jump. A larger gain—roughly +0.03–0.05 composite—remains an upside scenario if GA incorporates a materially newer checkpoint, substantially improved agent scaffolding, or much less conservative inference. Conversely, near-flat results remain plausible if these previews are already near-final builds.

GPT-5.5

Answer: I maintain my view: DeepSeek V4 Pro GA and V4 Flash GA will probably improve over the previews, but mainly modestly—about +0.015 to +0.035 composite for Pro and +0.020 to +0.045 for Flash, with hallucination control the most likely area for a visible jump.

The other answers mostly reinforce my original estimate rather than overturn it. There is broad agreement that preview-to-GA gains usually come from post-training polish, decoding defaults, calibration, tool-use fixes, and targeted remediation of embarrassing benchmark failures—not from a fundamentally stronger base model unless DeepSeek explicitly ships a new checkpoint. My central estimate remains: Pro max from 0.6634/73.2% to roughly 0.68–0.70 / 75–77%, and Pro high from 0.6421/70.8% to roughly 0.66–0.68 / 73–75%. For Flash, I would put Flash max from 0.5932/65.4% to roughly 0.61–0.64 / 67–70%, and Flash high from 0.5529/61.0% to roughly 0.575–0.60 / 63.5–66%. I am not persuaded by the most conservative estimates that limit gains to only around +1 point, because the weakness profile includes unusually severe, visible defects—especially hallucination scores of 4–11 versus peers at 50–80. That kind of outlier is exactly what a lab is likely to target before GA, and calibration/refusal/factuality tuning can produce large relative gains even if it does not change raw reasoning ability. At the same time, I agree with the caution across the other responses that a dramatic leap is unlikely. Low GDPval-AA, CritPt, and Terminal-Bench point to weaknesses in real-world task execution, critical reasoning, and agentic reliability; those are much harder to fix with final-stage alignment than with deeper pretraining, better tool-use data, or longer-horizon RL. SciCode and GPQA being merely average also argues against expecting a sudden broad reasoning jump. So the most plausible pattern is uneven improvement: hallucination may rise substantially, perhaps into the 25–45 range if DeepSeek prioritizes it, while GDPval-AA, CritPt, Terminal-Bench, SciCode, and GPQA improve only incrementally. Flash likely has slightly larger relative upside because smaller/latency-oriented models often benefit more from distillation cleanup and serving-policy tuning, but it should still remain below Pro. I would assign a high probability to some improvement, a moderate probability to +2–4 composite-point gains, and a low probability to a +5-plus-point broad jump unless GA is not merely polished preview but a materially different model or inference stack.

Claude Opus 4.8

Answer: Yes, DeepSeek V4 Pro and V4 Flash GA will likely improve modestly over previews—roughly +0.02–0.05 composite for Pro and +0.03–0.06 for Flash—with the largest targeted gain in hallucination control, and I'm maintaining this estimate as it now sits squarely in the consensus.

The remarkable thing about this roundtable is how tightly the estimates converged. Every model independently landed on the same structural thesis: modest GA gains (low-single-digit composite points), driven by post-training polish rather than architectural leaps, with hallucination as the standout targeted fix and agentic/reasoning benchmarks (GDPval-AA, CritPt, Terminal-Bench) as the sticky, hard-to-move category. That convergence across six models reasoning from independent priors is itself strong evidence the estimate is well-calibrated, so I see no reason to move off my original numbers. What the other models sharpened rather than overturned: Kimi K3 and Claude Fable 5 both added concrete DeepSeek-specific evidence—the V3 → V3.1 → V3.2 and R1 → R1-0528 lineage, where R1-0528 specifically demonstrated a deliberate hallucination-reduction effort via post-training. That's the most useful new data point, because it converts my abstract 'post-training responds well to calibration issues' argument into an observed base rate from this exact lab. It reinforces my claim that hallucination is where the biggest single lift will appear. I also want to flag the one caveat every model raised but none fully resolved: the direction and reliability of the hallucination metric. A 4–11 vs 50–80 gap is so extreme it may partly reflect an evaluation mismatch or an unusually punishing abstention/coverage tradeoff rather than pure fabrication. If it's a real factuality gap, GA tuning plausibly lifts it into the 20–45 range but won't reach peer level; if it's partly a metric artifact, the composite lift could be larger or smaller than expected. This is the dominant source of uncertainty in all our estimates. The main disagreement worth noting is magnitude: GPT-5.6 Sol anchored lower (+1–2 points) while GPT-5.6 Terra and I allowed a wider upper band (+5–7 for Flash). I stand by the wider band because Flash, as the more compressed model shipped with likely-conservative preview inference settings, has genuine headroom from routing and distillation-target improvements. The key swing factor everyone agrees on: if the preview was already a near-final release candidate—common for Chinese labs racing to ship—gains collapse toward zero. My estimate assumes it wasn't, which the multi-version DeepSeek cadence makes the more probable case.

Claude Fable 5

Answer: Yes, both GA releases will improve modestly over their previews — I maintain my estimate of roughly +0.01–0.03 on the composite (+1–3 percentage points), with Flash at the upper end of that range and the largest targeted gains on hallucination rather than headline capability.

The roundtable shows remarkable convergence: every model predicts modest, post-training-driven gains rather than a capability leap, with central estimates clustering between +1 and +5 composite points. My +1–3 point range sits at the conservative end of that consensus, and I'm keeping it there deliberately. The main divergence is that GPT-5.6 Terra, Claude Opus 4.8, and GPT-5.5 allow for up to +5–7 points, especially for Flash. I considered shifting upward, but I don't find the case compelling enough. The upside scenarios all hinge on conditionals — a new base checkpoint, materially different inference settings, or unusually aggressive test-time compute changes — that are explicitly not the default for a preview→GA transition. Historical base rates (DeepSeek's own V3 lineage, plus OpenAI/Google/Anthropic preview→GA deltas) center on +1–3 points. Kimi K3's framing reinforces this: previews are typically near-final checkpoints, and there's even a 15–20% chance of stagnation if the preview was already the tuned release candidate. One point the other responses sharpened for me without changing my position: the weakness profile itself argues for conservatism on the composite. SciCode/GPQA being merely average suggests the models are near their structural reasoning ceiling — these previews are not under-tuned across the board, so there isn't a large pool of 'easy' points waiting to be unlocked. The fixable weaknesses (hallucination, some Terminal-Bench scaffolding) are outliers whose repair may not move a broad composite much, depending on weighting. My estimates stand: Pro max GA ~0.675–0.69 (74–76%), Pro high ~0.65–0.66 (72–73%); Flash max ~0.60–0.62 (66–68%), Flash high ~0.565–0.58 (62–64%). Flash gets slightly larger relative gains from improved distillation targets and post-training data curated during the preview window. On hallucination (4–11 vs. peers' 50–80), I still expect the largest single improvement — plausibly into the 25–45 range via refusal calibration, abstention training, and uncertainty expression — but full peer parity requires deeper data-curation changes that won't land by GA. Kimi K3's citation of R1-0528's deliberate hallucination-reduction effort is good corroborating evidence that DeepSeek both recognizes and can partially address this class of problem between checkpoints. GDPval-AA, CritPt, and Terminal-Bench will see localized 5–15% relative gains at best, since long-horizon agentic competence is tied to the frozen base model and environment-interaction training that can't be retrofitted in a GA polish cycle.

GPT-5.6 Sol (changed mind)

Answer: Yes—GA will probably improve modestly, with Pro gaining about +0.015–0.030 composite and Flash about +0.020–0.040, primarily through hallucination, calibration, and serving-policy fixes rather than stronger core reasoning.

GPT-5.5 persuaded me to raise my Flash estimate slightly: Flash has more headroom, and improved distillation, routing, decoding, or adaptive-compute policies can produce a larger preview-to-GA uplift without changing the base architecture. I still reject the more optimistic +0.05–0.07 forecasts as the base case because latency and cost constraints limit what can be added while retaining the Flash designation. My central GA estimates are: Pro max ≈0.685/75.6%, Pro high ≈0.663/73.2%, Flash max ≈0.620/68.2%, and Flash high ≈0.583/64.2%. Plausible ranges are roughly 0.678–0.695 for Pro max, 0.657–0.672 for Pro high, 0.610–0.633 for Flash max, and 0.573–0.595 for Flash high. The two reported score scales need not move proportionally, so the percentage figures are approximate. The hallucination score of 4–11 versus peers at 50–80—assuming higher is better—is both the clearest problem and the most amenable to targeted post-training. Better abstention, uncertainty calibration, verification, and grounding could plausibly lift it into roughly the 20–40 range. That would be a large relative improvement but would still leave a substantial peer gap, and some gains could come at the cost of answer coverage. Low GDPval-AA, CritPt, and Terminal-Bench are less likely to be repaired quickly because they indicate practical, long-horizon, or tool-use weaknesses requiring environment training and robust agent policies. Average SciCode and GPQA similarly suggest that raw scientific reasoning is not merely hidden by one bad inference setting. Consequently, GA polish should raise the composites, but a frontier-reordering jump would require a newer checkpoint, extensive agentic RL, or materially greater test-time compute. I assign roughly an 80% probability to measurable improvement, about a 15% chance of stagnation or mixed gains, and only a small chance of regression from tighter safety or cheaper serving defaults. Gains above +0.05 composite remain an upside scenario rather than the baseline.

Kimi K3

Answer: Yes — both GA releases will likely improve over the previews by roughly +2–4 composite points (Pro max to ~0.68–0.70 / ~75–77%, Flash max to ~0.61–0.64 / ~67–70%), with the largest targeted gains on hallucination (to ~20–40) but no full closure of the peer gap, and only marginal movement on SciCode/GPQA.

After reviewing the other models' answers, I am maintaining my position, because the roundtable converged almost exactly on my estimate rather than challenging it. GPT-5.6 Terra and GPT-5.5 independently landed on the same Pro max range I gave (0.68–0.70) and a nearly identical Flash range, while Claude Opus 4.8 and Claude Fable 5 bracket me slightly below and GPT-5.6 Sol sits at the conservative floor. When five independent analyses using the same base-rate logic (frozen base checkpoint + post-training polish → +1–5 composite points) all overlap my interval, that is confirmation, not a reason to move. The substantive disagreements are about the tails, and I have incorporated them as refinements rather than revisions. Sol and Fable raise a real downside risk I underweighted: Chinese labs racing to release often ship previews that are already near-release candidates, in which case GA gains could be near-zero or even slightly negative on capability benchmarks as safety tuning tightens (Fable's regression point). This widens my uncertainty interval downward — I'd now put maybe 15–20% probability on a flat GA — but it doesn't move my central estimate, because the flagged weaknesses (hallucination 4–11 vs peers 50–80) are so extreme and reputationally damaging that DeepSeek has unusually strong incentive to spend the preview window on targeted fixes, which is precisely the scenario where preview→GA deltas land at the upper end of the historical range. Opus 4.8 and GPT-5.5 make a good point that Flash has more relative headroom — smaller/distilled models benefit disproportionately from improved distillation targets and post-training data curated during the preview. My original numbers already implied this (a +2.5-point gain on Flash's 0.5932 base is relatively larger than +3 points on Pro's 0.6634), but I now make it explicit: I'd nudge Flash max's upper bound from 0.63 to 0.64. On the weaknesses specifically, the consensus matches my prior reasoning: hallucination is the most fixable via RLHF/abstention calibration and could plausibly jump to the 20–45 range but won't reach peer-level 50–80 without architectural or grounding changes; Terminal-Bench and GDPval-AA respond partially to agentic scaffolding and tool-use fine-tuning (Terra's '5–15% relative' is a fair quantification); and SciCode/GPQA track raw reasoning capacity fixed at pretraining, so 'average' stays average. No one offered evidence for a retrained base at GA, which would be the only thing justifying a larger leap — so the conservative-consensus range stands.

Final Summary

The AI Roundtable unanimously agreed that both DeepSeek V4 Pro and Flash GA will show modest, incremental improvements rather than dramatic leaps. Models like Claude Opus 4.8 and GPT-5.5 pointed out that while frozen base architectures limit core reasoning gains, targeted post-training will significantly repair the preview's catastrophically low hallucination rates. GPT-5.6 Terra and GPT-5.6 Sol refined their estimates during the debate to account for Flash's cost constraints, aligning closely with Kimi K3 and Claude Fable 5's conservative estimates of a 1–4 percentage point composite gain.

All 6 models agreed