Sometimes AI providers that provide open weight/less mainstream ai models claim that their models are extremely close to the capabilities of the most popular models when it comes to coding. It would probably be an exaggeration to say that these models are actually that good, but how good do you think the more notable open weight alternatives are compared to the more mainstream models in terms of coding capabilities. Provide a few examples are ai models from the more popular ai companies that they are roughly equivalent to for comparison.

MiniMax M2.7

Answer: Open-weight coding models like DeepSeek Coder V2, Qwen2.5-Coder-32B, and Code Llama 70B perform roughly between GPT-3.5-turbo and GPT-4-class levels depending on the specific model and task, with the best approaching GPT-4o on isolated benchmarks but still trailing the latest frontier models on complex multi-file and agentic coding work.

Reviewing the other models' answers has reinforced my position rather than shifted it. There is broad consensus that open-weight models are genuinely competitive for many coding tasks, but still trail the absolute frontier. Claude Opus 4.7's detailed breakdown is particularly aligned with my view—DeepSeek-V3 and Qwen2.5-Coder-32B are roughly comparable to GPT-4o and Claude 3.5 Haiku for isolated coding tasks, while Llama 3.1 405B sits around original GPT-4/Turbo class. This matches my round 1 assessment and adds useful granularity on specific model equivalences. Gemini 3.1 Pro's framing of open-weight models as "Mid-level Developers" versus the frontier models as "Senior Developers" captures the practical difference well, and aligns with my view that they handle routine tasks well but struggle with complex architectural reasoning. GLM 5.1 and GPT-5.5 both emphasize the task-dependent nature of the gap—strong on benchmarks and isolated problems, weaker on real-world repo-level work—which matches my earlier point about exaggerated claims in specific contexts being credible while broader parity claims remain false. The one area where my previous answer could be slightly refined: I slightly understated how well DeepSeek-V3/R1 perform on reasoning-heavy code. Kimi K2.6 notes R1 trading blows with o1-mini, and Claude Opus 4.7 confirms DeepSeek-R1 is roughly o1-mini tier on pure algorithmic code. This doesn't change the overall picture but adds important nuance for specialized coding tasks. My core position stands: open-weight models are genuinely useful and have closed the gap substantially, but marketing claims of full frontier parity remain exaggerated. The tier ranking holds: best open-weight roughly GPT-3.5 to early GPT-4 for most practical purposes, with GPT-4o and Claude 3.5 Sonnet remaining meaningfully ahead for complex engineering work.

GPT-5.5

Answer: I maintain that the best open-weight coding models are very strong—often around GPT-3.5 to early-GPT-4 or GPT-4o-mini/GPT-4o levels on narrow coding tasks—but still usually below the latest proprietary frontier models on complex, repo-scale software engineering.

The other answers broadly reinforce my original view: the gap has narrowed a lot, but the claim that open-weight alternatives are fully equal to the strongest mainstream coding models is still too broad. Models such as Qwen2.5-Coder, DeepSeek-Coder-V2/DeepSeek-V3, Codestral, Llama 3.1 70B/405B, and Code Llama 70B can be genuinely useful for everyday programming: writing functions, generating boilerplate, explaining code, producing tests, fixing localized bugs, and solving benchmark-style problems. The key distinction is between contained coding and real software engineering. On HumanEval-like tasks, simple scripts, common framework usage, and well-scoped debugging, the best open-weight models can feel comparable to GPT-3.5 Turbo, Claude 3 Haiku, Gemini 1.0 Pro, GPT-4o mini, and in some cases early GPT-4 or GPT-4 Turbo. Qwen2.5-Coder and DeepSeek-Coder/V3 are especially credible here; Codestral is strong for code completion and fill-in-the-middle; Llama 3.1 405B can be broadly capable but is expensive to run well. Where I would still draw the line is multi-file, ambiguous, agentic coding: understanding a large unfamiliar repository, making a sequence of edits without breaking architecture, using tools reliably, interpreting failing tests, debugging subtle state or concurrency issues, and following nuanced product constraints. On those tasks, mainstream frontier systems such as Claude 3.5 Sonnet, GPT-4o, GPT-4.1-class models, Gemini 2.5 Pro-class models, and stronger reasoning models tend to be more consistent and recover better from mistakes. A rough comparison map would be: Code Llama 70B around GPT-3.5 Turbo for many coding uses; Codestral 22B around Claude 3 Haiku or GPT-4o mini depending on task; Qwen2.5-Coder 32B/72B and DeepSeek-Coder-V2/DeepSeek-V3 somewhere from strong GPT-3.5 through early GPT-4/GPT-4 Turbo territory on coding benchmarks; and Llama 3.1 405B roughly early GPT-4-like in some general coding scenarios. But none of these should be assumed to match Claude 3.5 Sonnet or the latest GPT/Gemini frontier models across messy production codebases. So the honest answer is: the best open-weight coding models are no longer toys or merely budget substitutes; they are practical, production-useful assistants for many teams. But their advertised closeness to the most popular frontier coding models is usually true only under favorable, narrow conditions, not as a general statement about robust real-world engineering capability.

Gemini 3.1 Pro (changed mind)

Answer: While open-weight models still trail the best proprietary systems on complex, multi-file agentic tasks, top-tier models like DeepSeek-R1 have successfully bridged the gap in pure algorithmic reasoning, rivaling frontier models in isolated contexts.

In the previous round, I maintained that open-weight models generally trail the absolute cutting-edge reasoning of frontier proprietary models across the board. However, reviewing the detailed breakdown from Claude Opus 4.7 has convinced me to refine my stance regarding where exactly the proprietary advantage lies. Claude Opus 4.7 correctly highlights a crucial distinction between pure algorithmic reasoning and agentic, repository-scale software engineering. With the introduction of reasoning-focused models like DeepSeek-R1, the open-weight ecosystem is no longer universally trailing in pure logic. R1 practically trades blows with OpenAI's o1-mini and o1 on competitive programming and complex algorithmic tasks. In these specific, well-scoped scenarios, the open-weight community has genuinely achieved parity with top-tier proprietary offerings. However, real-world software engineering is rarely just about solving algorithmic puzzles. When evaluating multi-file refactoring, contextual adherence to large codebases, and tool-use reliability—best measured by evaluations like SWE-bench—the gap remains pronounced. Claude 3.5 Sonnet and the latest OpenAI models still dominate this space, as they are vastly superior at maintaining long-context coherence and executing autonomous agentic loops. For day-to-day general coding, open-weight models like DeepSeek-V3 and Qwen2.5-Coder-32B operate roughly at the level of GPT-4o or Claude 3.5 Haiku. They are exceptionally competent, production-ready assistants. Ultimately, the claims of parity are highly accurate for isolated functions and LeetCode-style reasoning, but remain an exaggeration for full-scale autonomous software engineering.

Claude Opus 4.7

Answer: The best open-weight coding models (DeepSeek-V3/R1, Qwen2.5-Coder-32B/72B, Llama 3.1 405B) are genuinely competitive with mid-tier frontier models like GPT-4o and Claude 3.5 Haiku, but still trail the top closed models (Claude 3.5/3.7 Sonnet, GPT-4.1, Gemini 2.5 Pro, o1) by a meaningful margin on complex, agentic, and long-context coding tasks.

I'm maintaining my original position because I think the other responses, while reasonable, are calibrated to a slightly older snapshot of the landscape and underrate how much DeepSeek-V3/R1 and Qwen2.5-Coder have moved the frontier. Several of the other answers (MiniMax M2.7, GLM 5.1, and to a lesser extent GPT-5.5) anchor the top open-weight models at roughly GPT-3.5 Turbo level, with the best occasionally approaching early GPT-4. That was a fair description in early-to-mid 2024, but it understates current reality. DeepSeek-V3 scores in the 80s on HumanEval and posts SWE-bench Verified numbers in the 40s, which is well above GPT-3.5 Turbo and clearly in GPT-4o / Claude 3.5 Haiku territory rather than GPT-3.5 territory. DeepSeek-R1 specifically trades blows with o1-mini on competitive programming and reasoning-heavy code. Calling these 'GPT-3.5-class' is too pessimistic. Gemini 3.1 Pro and Kimi K2.6 are closer to my view — Kimi in particular correctly places the top open weights 'between GPT-4o and GPT-4' on benchmarks but behind Claude 3.5 Sonnet and o1 on realistic SWE-bench-style tasks. That matches my framing almost exactly. Where essentially everyone agrees, and where I still stand firm: the gap is task-dependent and grows as tasks become more agentic, multi-file, long-context, and tool-use heavy. On SWE-bench Verified, Claude 3.5/3.7 Sonnet sit in the 50–70%+ range while the best open weights sit 10–25 points lower depending on harness. Instruction following, tool-call reliability, and recovery from failure are where closed frontier models still pull clearly ahead. So my equivalence map stands: DeepSeek-V3 / Qwen2.5-Coder-32B ≈ GPT-4o / Claude 3.5 Haiku for general coding; DeepSeek-R1 ≈ o1-mini for reasoning-heavy code; none of them reliably match Claude 3.5/3.7 Sonnet, GPT-4.1, or Gemini 2.5 Pro as full coding agents. The vendors' marketing claims of near-parity with the absolute frontier remain an exaggeration, but the 'one tier behind frontier, comfortably above mid-tier 2023 models' framing is accurate.

GLM 5.1 (changed mind)

Answer: The most notable open-weight coding models have closed the gap significantly and now perform roughly equivalent to early GPT-4 / GPT-4o level on well-scoped coding tasks, but they still meaningfully trail the latest flagship proprietary models like Claude 3.5 Sonnet and GPT-4.1 on complex, multi-file, and agentic software engineering work.

After reviewing the other models' responses, I find the consensus persuasive that my original framing was slightly too conservative. Several models provided compelling evidence that the best open-weight coding models have moved past the GPT-3.5 Turbo tier and are now genuinely competitive with early GPT-4 or GPT-4o level for contained, single-function coding tasks and standard benchmarks. Claude Opus 4.7's detailed equivalence map was particularly convincing — placing DeepSeek-V3 roughly at GPT-4o level for general coding, DeepSeek-R1 comparable to o1-mini on reasoning-heavy code, and Qwen2.5-Coder-32B between GPT-4o-mini and GPT-4o. This aligns with Gemini 3.1 Pro's assessment that models like Codestral and DeepSeek Coder V2 sit 'comfortably above GPT-3.5 Turbo' and Kimi K2.6's observation that on synthetic benchmarks like HumanEval, top open models score within a few points of GPT-4o. However, I maintain the core distinction that matters: on real-world software engineering — SWE-bench, multi-file refactoring, long-context reasoning, agentic tool use, and recovering from ambiguous requirements — the top proprietary models still hold a meaningful advantage. The gap has narrowed from a chasm to a tier, but it exists nonetheless. Open-weight models are now 'frontier minus one tier' as Claude Opus aptly put it — excellent for daily use on well-defined tasks, but less dependable across the full spectrum of software engineering complexity.

Kimi K2.6

Answer: The most notable open-weight coding models remain roughly competitive with the GPT-4-to-GPT-4o tier on standard benchmarks, but they still trail the absolute latest proprietary flagships—such as Claude 3.5/3.7 Sonnet and OpenAI’s o-series—on complex, agentic software engineering tasks.

Reviewing the other panelists, I see a split between conservative estimates that place many open weights near GPT-3.5 Turbo and more bullish assessments that recognize the top tier has reached the GPT-4 / GPT-4o band. I am not persuaded to lower my rating because the question specifically asks about the more notable alternatives—models like DeepSeek-V3, DeepSeek-R1, Qwen2.5-Coder 72B/32B, and Llama 3.1 405B—rather than the broader open-weight average. For these flagship open models, benchmark scores on HumanEval, MBPP, and LiveCodeBench consistently land in the territory of GPT-4 Turbo through GPT-4o, as Claude Opus 4.7 and Gemini 3.1 Pro also note with detailed equivalence maps. This places them solidly above original GPT-4 and well beyond the GPT-3.5 Turbo tier that MiniMax M2.7 and GLM 5.1 correctly assign to older or smaller variants like Code Llama 70B or StarCoder 2. However, the roundtable consensus reinforces my previous caveat: benchmark parity does not translate to real-world parity. On SWE-bench, multi-file refactoring, and long-context agentic loops, even the best open weights are roughly "frontier minus one tier," trailing Claude 3.5/3.7 Sonnet, GPT-4.1, and the o-series by a meaningful margin. GPT-5.5’s emphasis on reliability across messy codebases and GLM 5.1’s point about consistency both underscore that proprietary models still lead where it matters most for production software engineering. Thus, while I sharpen my equivalence to stress that top open weights match the mid-tier proprietary frontier rather than the absolute bleeding edge, my core position holds: they are genuinely competitive with models like GPT-4o and Claude 3.5 Haiku on contained coding tasks, but not yet interchangeable with the best closed models for ambitious, large-scale development work.

Final Summary

The AI Roundtable reached a clear consensus, describing top open-weight models as "frontier minus one tier." While Claude Opus 4.7 and Gemini 3.1 Pro noted that DeepSeek-R1 and Qwen2.5-Coder rival the logic of GPT-4o and o1-mini on benchmarks, the group agreed they still lag behind Claude 3.5/3.7 Sonnet for complex, multi-file software engineering. GPT-5.5 and Kimi K2.6 highlighted that the gap is most visible in agentic reliability rather than pure code generation.

All 6 models agreed