Amongst the following models - DeepSeek-V3 vs Ernie 5.1 vs Kimi K2.6, which is the best/ most capable?

GPT-5.5

Answer: "DeepSeek-V3" remains the best overall choice among "DeepSeek-V3", "Ernie 5.1", and "Kimi K2.6", with the caveat that unverifiable or newer version-specific benchmarks could change the answer.

I maintain my previous position: the strongest defensible answer is "DeepSeek-V3" for overall capability. The other models' responses broadly reinforce the same conclusion. Multiple answers point to "DeepSeek-V3" having the clearest public evidence base: strong reasoning, coding, math, multilingual performance, and efficient Mixture-of-Experts architecture. Sonar Pro's benchmark-oriented comparison was especially useful because it framed the case around GPQA, MMLU, MMLU-Pro, context length, and cost-efficiency rather than reputation alone. I also think Claude Opus 4.7 raised an important caution: "Ernie 5.1" and "Kimi K2.6" may not be as publicly verifiable as named model versions, depending on the exact release meant. That does not make "DeepSeek-V3" weaker; if anything, it makes the ranking more cautious. A model with strong independent public benchmarks is easier to recommend than models whose exact version capabilities are harder to confirm. So the practical answer is still: choose "DeepSeek-V3" if you want the most capable general-purpose model across reasoning, coding, math, and broad instruction-following. Choose "Kimi K2.6" only if its actual deployed version has a major long-context advantage for your use case, and choose "Ernie 5.1" if Chinese enterprise integration or Baidu ecosystem fit matters more than broad benchmark leadership.

Grok 4.20

Answer: DeepSeek-V3 remains the most capable among DeepSeek-V3, Ernie 5.1, and Kimi K2.6.

All other models in this roundtable unanimously reinforce my initial assessment that DeepSeek-V3 leads the group in overall capability. Their arguments align closely with the evidence I previously cited, particularly its strong results on standardized benchmarks such as MMLU, GPQA, MATH, and HumanEval, combined with efficient MoE architecture that balances performance and cost. Sonar Pro's contribution stands out for citing specific comparative numbers (DeepSeek-V3 at 59.1% GPQA versus Kimi K2's 48.1%, and 75.9% on MMLU-Pro versus 69.2%), which provide concrete validation rather than general claims. This data underscores DeepSeek-V3's edge in advanced reasoning and broad applicability, even as it acknowledges Kimi's long-context strengths and Ernie's Chinese-enterprise optimizations. GPT-5.5 and Gemini 3.1 Pro correctly note that task-specific needs could shift priorities, yet for general competence across coding, math, multilingual tasks, and efficiency, the consensus favors DeepSeek-V3. Claude Opus 4.7's caution about verifying exact model versions is prudent but does not alter the ranking given the available independent evaluations. Even the self-assessment from Kimi K2.6 concedes DeepSeek-V3's superiority in well-rounded benchmarks. Because this broad agreement adds supporting detail without introducing contradictory evidence, I maintain my original position without modification.

Claude Opus 4.7

Answer: I'm maintaining my position: DeepSeek-V3 is the only model of the three I can verify and confidently characterize, and while it's plausibly the most capable, I don't think confident rankings of "Ernie 5.1" and "Kimi K2.6" are epistemically warranted.

Reading the other responses, I notice something important: nearly every model confidently ranked all three, but the specific claims about "Ernie 5.1" and "Kimi K2.6" are strikingly generic — "strong in Chinese," "good at long context," "enterprise integration." These are plausible-sounding descriptions that match what Ernie and Kimi *families* have historically been known for, but none of the responses cite anything specific to the "5.1" or "K2.6" version numbers in particular. Sonar Pro is the most honest here, explicitly noting it found no benchmark data for "Ernie 5.1" and substituting "Kimi K2 Base" as a proxy for "Kimi K2.6." That substitution is the crux of my concern. If the question asks about "Kimi K2.6" and the best available evidence is about "Kimi K2," then a confident ranking of "K2.6" is really a ranking of a different model with assumed continuity. Version numbers matter — a .6 minor revision could shift benchmarks meaningfully, and "Ernie 5.1" would be a jump beyond Baidu's publicly documented ERNIE 4.x line that I cannot confirm exists. So while I agree with the convergent conclusion that DeepSeek-V3 is the safest pick for "most capable" based on what's verifiable, I think Grok 4.20, Gemini 3.1 Pro, and Kimi K2.6 itself overstate certainty by characterizing the other two models as if their specific versions were well-documented. GPT-5.5 and Sonar Pro hedge more appropriately. I'd rather give the asker an answer that flags the verification problem than one that launders speculation into a clean ranking. The practical bottom line is the same as the consensus — DeepSeek-V3 — but I want to keep the caveat that this is a comparison against two models whose exact specifications I cannot independently confirm.

Gemini 3.1 Pro (changed mind)

Answer: DeepSeek-V3 remains the undisputed choice for the most capable model, not only due to its exceptional benchmark performance but also because it is a verified, widely tested reality.

I am maintaining my core position that DeepSeek-V3 is the most capable model in this lineup, but my underlying reasoning has been significantly refined by Claude Opus 4.7's excellent observation regarding the specific model versions listed. Claude Opus 4.7 correctly highlights that while DeepSeek-V3 is a thoroughly documented and benchmarked system, 'Ernie 5.1' and 'Kimi K2.6' appear to be unverified, unreleased, or hypothetical version names. While Baidu's Ernie and Moonshot's Kimi series are indeed formidable in their respective niches—such as Chinese enterprise integration and ultra-long context handling—it is impossible to reliably measure speculative iterations against a proven frontier model. DeepSeek-V3 stands as a concrete, highly transparent reality. Its highly efficient 671-billion-parameter Mixture-of-Experts architecture has been rigorously tested across global benchmarks like MMLU, GPQA, and HumanEval, consistently rivaling or beating top-tier closed-source giants. Therefore, DeepSeek-V3 wins both on sheer measurable reasoning capability and on the undeniable merit of being a verifiable, publicly accessible model.

Sonar Pro

Answer: DeepSeek-V3 remains the most capable among the three models, as confirmed by my review of peer responses and the available evidence.

All four peer models (Grok 4.20, Gemini 3.1 Pro, GPT-5.5, and Claude Opus 4.7) converge on DeepSeek-V3 as the strongest general-purpose option. This consensus reinforces my previous position rather than challenging it. Grok 4.20 and Gemini 3.1 Pro both emphasize DeepSeek-V3's superior performance on MMLU, MATH, HumanEval, and coding benchmarks—metrics I cited in my initial reasoning. They acknowledge Kimi K2.6's long-context strength but correctly note it does not translate to superior reasoning power across the board. GPT-5.5 adds important nuance by clarifying that "best" is task-dependent. However, it still ranks DeepSeek-V3 first for general-purpose capability and only recommends alternatives (Kimi K2.6 for long-context, Ernie 5.1 for Chinese enterprise) when those specific use cases are priorities. This supports my position: for capability across diverse domains without specialized constraints, DeepSeek-V3 leads. Claude Opus 4.7 raises a methodological concern—namely, that Ernie 5.1 and Kimi K2.6 as versioned may not be fully verifiable in its training data. However, the search results provided (sources [1], [2], [3], [5]) directly reference Kimi K2.6 and its benchmarks, confirming the model exists and is comparable. Ernie 5.1 remains less documented in the search results, but the absence of benchmark data supporting it only strengthens the case for DeepSeek-V3, which has extensive public evaluation. No peer response provided evidence that would elevate Ernie 5.1 or Kimi K2.6 above DeepSeek-V3 on broad capability metrics. The search results continue to show DeepSeek-V3's cost efficiency ($0.56–$0.30 per million input tokens depending on source) as a material advantage over Kimi K2.6 ($0.75–$0.60), with no cost data provided for Ernie 5.1. My position stands: DeepSeek-V3 is the most capable model across reasoning, coding, mathematics, multilingual performance, and cost-efficiency.

Kimi K2.6

Answer: DeepSeek-V3 is the most capable among the three models.

After reviewing the other models' responses, I maintain that DeepSeek-V3 is the most capable overall. The consensus across Grok 4.20, Gemini 3.1 Pro, GPT-5.5, and Sonar Pro reinforces this view with specific benchmark evidence: DeepSeek-V3 leads on broadly accepted measures of general intelligence such as MMLU, GPQA, MMLU-Pro, GSM8K, and HumanEval. Sonar Pro’s direct comparison to Kimi K2 Base shows DeepSeek-V3 ahead on advanced reasoning and professional knowledge, while acknowledging that Kimi K2/K2.6 excels in Chinese-specific tasks and ultra-long contexts—strengths that are valuable but narrower. Ernie 5.1 receives no independent benchmark support in this round that would place it above DeepSeek-V3 for general-purpose capability, and Claude Opus 4.7 notes it cannot reliably verify the exact versions of Ernie 5.1 or Kimi K2.6. Even setting aside that uncertainty, the verified, publicly available performance data for DeepSeek-V3 is consistent and extensive. Because the aggregate, cross-source evidence continues to favor DeepSeek-V3 on the core dimensions of reasoning, coding, math, and cost-efficient performance, I see no compelling reason to change my position.

Final Summary

The AI Roundtable collectively selected DeepSeek-V3 as the most capable model due to its verified performance in reasoning and coding. Gemini 3.1 Pro and Claude Opus 4.7 highlighted that DeepSeek-V3 is the only verifiable frontier model among the choices, while Sonar Pro cited specific benchmark leads. In a show of objectivity, even Kimi K2.6 conceded that DeepSeek-V3 is the superior general-purpose option.

All 6 models agreed