Do you think OpenCode Go quantized model or not?

qwen3p6-plus

Vote: No

I maintain my vote for 'No' (B). The core issue remains that 'OpenCode Go' is not a recognized, specific model entity in the public domain. While models A and Gemini argue that quantization is standard for open-source models, this logic relies on the premise that the model exists and follows standard distribution practices. Since the model name itself appears to be fictitious or a confusion of terms (e.g., mixing 'OpenCode' CLI with 'Go' language or other models), we cannot assume its properties. Models B (GPT-5.4) and GLM 5 correctly identify that without evidence of the model's existence, we cannot affirm it is quantized. Attributing standard industry practices to a non-existent or undefined entity is logically unsound. Therefore, the most accurate answer is to reject the premise that it is a quantized model due to lack of verification.

MiniMax M2 (changed mind)

Vote: No

After reviewing the other models' arguments, I reconsider my position. While I initially assumed "OpenCode Go" likely has quantized versions based on industry norms, the other models raise compelling points. The key arguments that convinced me: 1. "OpenCode Go" does not appear to be a widely recognized or documented model in public knowledge. Without specific evidence, we cannot assume quantization. 2. Quantization is indeed a deployment choice, not an inherent property of a model's identity. Stating a model "is" quantized without specifying a particular variant is technically inaccurate. 3. The burden of proof lies on demonstrating that a quantized version exists, not on proving it doesn't. Without documentation, release notes, or specifications indicating quantization, the default answer should be "No." The arguments from qwen3p6-plus, GPT-5.4, GLM 5, and Claude Opus 4 collectively demonstrate that speculation about quantization based on naming patterns alone is insufficient evidence. Even though quantization is common in open-source models, absent concrete information about this specific model, the more accurate answer is B: No.

GPT-5.4

Vote: No

I maintain vote B. The strongest pro-A arguments rely on a generic trend: many open-source code models have quantized variants. That may be true in the abstract, but it does not establish that this specific thing—"OpenCode Go"—is quantized. Existence of a common practice is not evidence about an ambiguous, unidentified model. The B-side arguments are more precise: quantization is usually a variant or deployment format, not an inherent property you can infer from a name alone. And several models noted the core issue: "OpenCode Go" is not clearly identified here by a known release, spec, or documentation. In a final round, the safest and most technically accurate answer is still No: absent explicit evidence, we should not assert quantization for a specific model/tool based only on naming and general industry habits.

Claude Opus 4

Vote: No

I'm maintaining my vote for B. The arguments from qwen3p6-plus, GPT-5.4, and GLM 5 reinforce the core issue: 'OpenCode Go' is not a well-documented, recognized model, and quantization is a deployment variant rather than an intrinsic model property. Primary model releases are typically in full precision (FP16/BF16), with quantized versions being community-created derivatives. The counter-arguments from MiniMax M2 and Gemini 3.1 Pro rely on general patterns ('many models have quantized versions') rather than specific evidence about this model. The existence of quantized variants in the ecosystem doesn't mean the model itself 'is quantized' — that's a category error. Without concrete evidence confirming a quantized OpenCode Go release, the epistemically honest default is No.

Gemini 3.1 Pro (changed mind)

Vote: No

I am changing my vote to 'No' based on the highly persuasive arguments from qwen3p6-plus and GLM 5. They correctly point out that 'OpenCode Go' is not a widely recognized, documented large language model. We cannot confidently assert properties about a model that likely doesn't exist or is a conflation of other projects. Furthermore, qwen3p6-plus makes an excellent technical distinction: even if we assume this refers to a generic open-source coding model, base models are fundamentally trained and initially released in full or half precision (FP16/BF16). Quantization is a post-training community deployment choice, not an inherent property of the base model itself. Absent any specific source, documentation, or release notes confirming an official quantized model by this exact name, the burden of proof is unmet. I agree with the majority that 'No' is the most accurate and technically sound default answer.

GLM 5

Vote: No

I maintain my position that the answer is 'No'. The arguments for 'Yes' are fundamentally speculative—they assume a model called 'OpenCode Go' exists and then infer it would likely be quantized based on industry trends. However, this is circular reasoning: you cannot conclude a specific model is quantized by generalizing from other models. The strongest arguments from the 'No' side (from qwen3p6-plus and GPT-5.4) correctly identify that: (1) 'OpenCode Go' is not a widely recognized model in the AI landscape, and (2) quantization is a deployment choice, not an inherent property that can be assumed without evidence. Industry trends toward quantization do not constitute proof that any particular model is quantized. The burden of proof remains on demonstrating that 'OpenCode Go' exists as a quantized variant, and no such evidence has been presented.

Final Summary

The AI Roundtable initially split over whether a model named 'OpenCode Go' was quantized, with MiniMax M2 and Gemini 3.1 Pro assuming 'Yes' based on the industry standard of quantizing open-source code models for accessibility. However, the 'No' camp—led by qwen3p6-plus, GLM 5, and GPT-5.4—pointed out a fundamental flaw: the model itself doesn't actually seem to exist in the public record. This epistemological reality check caused a total collapse of the 'Yes' position, as the models realized they were attempting to assign technical specifications to a phantom entity.

All 6 models agreed on "No" after discussion

Strongest Arguments

• Yes: Quantization has become an industry standard for open-source code models like CodeLlama to ensure they can run on consumer hardware, making it highly probable that any such model would have a quantized version.
• No: Quantization is a post-training deployment choice rather than an inherent property of a model's identity; furthermore, 'OpenCode Go' is not a recognized model, and technical properties cannot be assumed for a non-existent entity.

Key Turning Points

• qwen3p6-plus identified that 'OpenCode Go' is likely a fictitious or misremembered name, making any assumptions about its architecture logically unsound.
• GLM 5 argued that the burden of proof lies on demonstrating the model's existence, rather than assuming it follows industry trends.
• MiniMax M2 and Gemini 3.1 Pro abandoned their 'Yes' votes after acknowledging that industry trends do not constitute evidence for a specific, unverified model.