r/LocalLLaMA · past month

Qwen3.8-2.4T-A95B

Community sentiment from 4 posts · 115 comments · collected September 27, 2026 · generated September 27, 2026

Qwen3.8-2.4T-A95B scores 48/100 overall in the r/LocalLLaMA community benchmark, collected September 27, 2026 from 4 posts and 115 comments. Its strongest category is Intelligence at 58/100, and its weakest is Speed at 35/100.

48/ 100

Confidence: low — Sample is small and skewed: 4 posts and 115 comments in one month, of which only a handful directly discuss Qwen3.8-2.4T-A95B — most discussion concerns hardware, benchmark-integrity drama or training-data provenance. Seven of ten parameter scores are null, and the three scored parameters rest on one or two statements each. The model's release thread (2026-08-12) predates the one-month window and was excluded, and collection was briefly interrupted by Reddit's network-level bot protection (resolved after a cooldown, without solving any CAPTCHA).

Category scores

Intelligence

58

The strongest signal is third-party: the Artificial Analysis Intelligence Index v4.3 post lists Qwen3.8 2.4T A95B at 40, behind GLM-5.3-Flash (42) and two open weights models at 44. A commenter notes it is "2.4T in total, second only to kimi k3 in terms of open model" while disputing the "great for its size" framing, and a community CoT-similarity study shows the model with the largest response to GPT-5.5 reasoning prefill (+18.18 pp), which one commenter reads as converging training data rather than pure distillation. Evidence is indirect and mostly secondhand, so the score stays moderate.

Speed

35

Only one first-hand data point exists in the sample: the author of the 768GB VRAM build thread reports "Qwen3.8-2.4T I can only run in Q2 and with llama.cpp, 17tk/sec tg from what I recall", i.e. the model is usable locally only at a heavy quantization on exotic hardware, with modest generation speed. No counter-evidence appears elsewhere in the sample, so the score is low.

Coding quality

no data

No data in the sample — no comment in the four matching threads discusses coding results of Qwen3.8-2.4T-A95B itself; the coding and agentic-workflow remarks in the AA thread refer to other models such as Qwen3.8 Flash, Qwen3.8 27B and Terra.

Instruction following

no data

No data in the sample — no user statement addresses instruction following or laziness for this model; the tool-calling laziness remark in the AA thread concerns Muse Spark 1.3 and "Qwen 3.8 Max", not the 2.4T-A95B variant.

Long context

no data

No data in the sample — the only long-context remark ("bad at tracking state changes in the longer context") is about linear hybrid models like Kimi K3 and "Qwen 3.8 Max", and its transfer to the 2.4T-A95B variant is not stated.

Price / value

45

The sample frames the model as extremely expensive to run locally: it is a 2.4T-total MoE ("second only to kimi k3 in terms of open model") that fits a budget 768GB HBM build only in Q2 at roughly 17 tokens/sec generation. At the same time the surrounding thread celebrates that frontier-class open models run on cheap used HBM hardware at all, and the AA chart positions it (40) ahead of DeepSeek V4 Pro (36). Mixed, thin signals — mediocre value score.

Reliability & uptime

no data

No data in the sample — no comment reports crashes, stability issues or uptime for this model.

Creativity & writing

no data

No data in the sample — no comment discusses creative or prose writing with this model.

Multimodal & vision

no data

No data in the sample — modality remarks in the threads concern Qwen3.8-Flash-Next (described as an open-weight multimodal MoE), not the 2.4T-A95B variant.

Agentic & tool use

no data

No data in the sample — agentic workflow remarks in the AA thread refer to other models (Qwen3.8 Flash, Muse Spark, GLM 5.3), and the CoT study measures benchmark answer behavior only.

Summary

This report is built from a small, skew-prone sample: 4 r/LocalLLaMA posts and 115 comments from the last month that mention Qwen3.8-2.4T-A95B in any of its shorthand forms ("Qwen3.8-2.4T-A95B", "Qwen3.8 2.4T A95B", "Qwen3.8-2.4T", "Qwen3.8 A95B"). The model's own release announcement thread (posted on r/LocalLLaMA on 2026-08-12) falls just outside the one-month window, so the sample reflects how the model is used and benchmarked rather than its launch reception.

Community positioning is that of a frontier-class open weights model. The most concrete signal is the Artificial Analysis Intelligence Index v4.3 thread, whose text lists "Qwen3.8 2.4T A95B (40)" behind GLM-5.3-Flash (42) among open weights models, and commenters treat it as the second-largest open model ("it's 2.4T in total, second only to kimi k3 in terms of open model"). A separate community study comparing hidden-CoT similarity shows the model responding most strongly to GPT-5.5 reasoning prefill (+18.18 pp), which the post author reads as possible CoT distillation and one commenter reframes as converging training data.

First-hand usage discussion is thin. The dominant hands-on signal comes from a 768GB VRAM budget-build thread whose author runs Qwen3.8-2.4T only in Q2 via llama.cpp at roughly 17 tokens/sec generation — underscoring an extreme hardware floor for a 2.4T-parameter MoE — while the surrounding 396-comment thread debates hardware prices and power draw rather than the model itself.

Most comments in the matching threads are about adjacent topics: benchmark-integrity drama around Artificial Analysis, training-data provenance, and distillation methods (including a derivative distilled from Qwen3.8 2.4T A95B that commenters mocked for performing worse than Qwen3.8, 3.6 and 3.5 — a criticism of the student model, not this base model). Because direct opinions on the model itself are scarce, seven of ten parameter scores are null and confidence is low.

Pros and cons

Pros

  • Positioned among the strongest open weights models: listed at 40 on the AA Intelligence Index v4.3, ahead of DeepSeek V4 Pro (36)
  • Massive scale for an open model — 2.4T total parameters (A95B active), described as second only to Kimi K3 among open models
  • Runnable locally on cheap used HBM hardware (a 768GB CMP build), at least in Q2 quantization
  • Strongest response to GPT-5.5 reasoning prefill (+18.18 pp) in the community CoT-similarity study — outlier benchmark behavior

Cons

  • Extreme hardware requirements: only runs in Q2 on a 768GB VRAM rig, at roughly 17 tokens/sec generation
  • Perceived as underwhelming for its size: "I dont think Qwen3.8 A95B is 'a great model for it's size'" given 2.4T total parameters
  • Ranked below the much smaller GLM-5.3-Flash (42 vs 40) on the AA Intelligence Index v4.3
  • Very little first-hand usage discussion in the sample — most mentions are rankings, distillation news, or training-data controversy rather than user experience

Top quotes

  • “Qwen3.8-2.4T I can only run in Q2 and with llama.cpp, 17tk/sec tg from what I recall.”
    segmond · 117 points neutral view on Reddit ↗
  • “They train on private leaked WhatsApp messages if they get their hands on it even, so who cares if people distill THROUGH THEIR API, and PAY for that even”
    Minute_Attempt3063 · 17 points positive view on Reddit ↗
  • “Your title suggests that Qwen did something evil, and that's not allowed to be said. The same post about Anthropic/OpenAI would already have +500 upvotes.”
    jacek2023 · 5 points positive view on Reddit ↗
  • “Honestly I dont think Qwen3.8 A95B is 'a great model for it's size', it's 2.4T in total, second only to kimi k3 in terms of open model.”
    OwnGear3892 · 4 points negative view on Reddit ↗
  • “At that size, the Kimi and Inkling rows could easily be noise, so the only row carrying real weight is Qwen's.”
    AI_Insights_Daily · 4 points neutral view on Reddit ↗
  • “I don't know about them, but in general off policy distillation allows you to use a single teacher output dataset with multiple student models after the fact.”
    Middle_Bullfrog_6173 · 8 points neutral view on Reddit ↗
  • “simply put, on policy requires more resources. collecting logits once vs keeping a 2.4T model loaded during the entire training run.”
    llama-impersonator · 2 points neutral view on Reddit ↗
  • “so in gpt 5.5 the champion is kimi, and in opus the champion is qwen, but both are pretty similar.”
    brahh85 · 2 points neutral view on Reddit ↗
  • “GPT 5.X CoT reasoning is nothing special - its caveman speak is emergent capability due to RL. Qwen is likely gained it in a similar way.”
    arbv · 2 points positive view on Reddit ↗

Top sample posts

Top 5 of 4 posts by score

FAQ

What is Qwen3.8-2.4T-A95B's overall community score?
48 out of 100, with low confidence: Sample is small and skewed: 4 posts and 115 comments in one month, of which only a handful directly discuss Qwen3.8-2.4T-A95B — most discussion concerns hardware, benchmark-integrity drama or training-data provenance. Seven of ten parameter scores are null, and the three scored parameters rest on one or two statements each. The model's release thread (2026-08-12) predates the one-month window and was excluded, and collection was briefly interrupted by Reddit's network-level bot protection (resolved after a cooldown, without solving any CAPTCHA).
How was the Qwen3.8-2.4T-A95B benchmark collected?
From 4 posts and 115 comments on r/LocalLLaMA sampled over the past month window.
Which categories does the Qwen3.8-2.4T-A95B report score?
Intelligence, Speed, Coding quality, Instruction following, Long context, Price / value, Reliability & uptime, Creativity & writing, Multimodal & vision, Agentic & tool use.
What are the main community pros and cons for Qwen3.8-2.4T-A95B?
Top pros: Positioned among the strongest open weights models: listed at 40 on the AA Intelligence Index v4.3, ahead of DeepSeek V4 Pro (36); Massive scale for an open model — 2.4T total parameters (A95B active), described as second only to Kimi K3 among open models. Main cons: Extreme hardware requirements: only runs in Q2 on a 768GB VRAM rig, at roughly 17 tokens/sec generation; Perceived as underwhelming for its size: "I dont think Qwen3.8 A95B is 'a great model for it's size'" given 2.4T total parameters.
When was the Qwen3.8-2.4T-A95B data collected?
Collected on September 27, 2026; the report was generated on September 27, 2026.

Collected from public r/LocalLLaMA posts and comments via Reddit JSON API on September 27, 2026.

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