Social·Community
Local Qwen3.8 27B Q6 and Strata Coder tie or trail Claude Opus 4.6 on three Python coding tasks in single-run test
A developer ran the same three Python tasks (log analyzer, parallel process manager, toy interpreter) through Claude Opus 4.6 and two local Qwen models, scoring with 162 hidden tests plus a fixed code-review checklist. The Qwen3.8-Flash-Next 'Coder' matched Opus 4.6 at 92.7/100 overall, while a 27B Q6 quant scored 87.0. The result suggests small local models can be viable substitutes for subscription coding APIs on small-scale tasks.
79 engagement1 sourceSat, Oct 3, 2026, 04:30 PM
AI Overview
- Redditor scored three Python tasks (log analyzer, parallel job manager, toy interpreter) with 162 hidden tests: Opus 4.6 92.7, Strata Coder 92.7, Qwen3.8 27B Q6 87.0
- Hidden tests were near-even: Opus 4.6 passed 162/162, Strata Coder 161, Qwen 27B 160; both locals beat Opus on the easy task (97 and 92 vs 88)
- Local setup: i5-14400, 48 GB RAM, dual 16 GB RTX 5060 Ti; Qwen 27B Unsloth Q6 ran at 50 tok/s, Strata Coder 50-90 tok/s (avg 60-65)
- Reviewer judged Opus 4.6's code cleaner and more maintainable, while the Strata Coder crashed less on strange inputs; review done by AI (Claude Fable 5.1)
- Caveats: single run per model, Coder's first run discarded after RAM exhaustion, Python-only small tasks; author notes newer Claude versions scored higher in his full test
Related
Social·Community
I gave 5 AI models the same 3 coding assignments and graded them like a teacher. Claude Opus 5.5 came first, Claude Fable 5.1 second
A Reddit user ran three progressively harder coding assignments through five AI models with identical instructions, one attempt each, then graded them with 162 secret tests plus manual review. Claude Opus 5.5 scored 98.7, Fable 5.1 96.5, Sonnet 5 95.0, Opus 4.6 92.7, and locally-run Qwen 3.8 27B 87.0. All models passed nearly all secret tests, so scores separated on robustness, code tidiness, and note honesty rather than correctness.
A Reddit user ran three progressively harder coding assignments through five AI models with identical instructions, one attempt each, then graded them with 162 secret tests plus manual review. Claude Opus 5.5 scored 98.7, Fable 5.1 96.5, Sonnet 5 95.0, Opus 4.6 92.7, and locally-run Qwen 3.8 27B 87.0. All models passed nearly all secret tests, so scores separated on robustness, code tidiness, and note honesty rather than correctness.
7 engagement1 sourceredditWed, Sep 23, 2026, 07:43 AM
Developments·Benchmark
Qwen 3.8 27B matches Gemini 2.5 Pro on Aider benchmark
A user benchmarked Qwen 3.8 27B FP8 with FP8 KV cache and 256K context on the Aider coding benchmark, scoring 72.9. This matches Gemini 2.5 Pro's score from April 2025 and beats Claude Opus 4 and DeepSeek R1, highlighting the model's strong coding performance on a MacBook.
A user benchmarked Qwen 3.8 27B FP8 with FP8 KV cache and 256K context on the Aider coding benchmark, scoring 72.9. This matches Gemini 2.5 Pro's score from April 2025 and beats Claude Opus 4 and DeepSeek R1, highlighting the model's strong coding performance on a MacBook.
74 engagement1 sourceredditMon, Aug 24, 2026, 07:56 AM
Products·concept
Self-Directed Agent harness: small local LLM autonomously builds full apps unattended
An autonomous coding harness that runs a small open-weight LLM (Qwen 3.8 27B) locally on a single RTX5090 for 24 hours, building a working Postgres-SpringBoot-React spreadsheet application with no human intervention. The only internet access is installing software onto a bare Ubuntu VM. It demonstrates that a smart harness plus a non-frontier model can produce real complex software unattended.
An autonomous coding harness that runs a small open-weight LLM (Qwen 3.8 27B) locally on a single RTX5090 for 24 hours, building a working Postgres-SpringBoot-React spreadsheet application with no human intervention. The only internet access is installing software onto a bare Ubuntu VM. It demonstrates that a smart harness plus a non-frontier model can produce real complex software unattended.
5 engagement1 sourceredditTue, Sep 22, 2026, 11:31 PM
Social·Community
I think I figured out why Claude stops thinking in long chats: model switching acts like garbage collection
A Reddit user reports Claude Opus/Fable models stop extended thinking mid-conversation despite Max thinking settings, and proposes a mechanism: retained hidden thinking blocks fill the context until the model sacrifices reasoning on new turns. Switching models appears to reset the behavior, possibly by discarding model-specific reasoning history, with Opus 4.6's 500K window acting as an accidental compactor.
A Reddit user reports Claude Opus/Fable models stop extended thinking mid-conversation despite Max thinking settings, and proposes a mechanism: retained hidden thinking blocks fill the context until the model sacrifices reasoning on new turns. Switching models appears to reset the behavior, possibly by discarding model-specific reasoning history, with Opus 4.6's 500K window acting as an accidental compactor.
24 engagement1 sourceredditFri, Sep 11, 2026, 07:21 PM
Developments·Benchmark
Community benchmark tests Qwen3.8-Flash-Next NVFP4 on local agentic coding
A community benchmark run compares the NVFP4-quantized Qwen3.8-Flash-Next against 27B-class and other local models on agentic coding tasks. Results are published on a public local-LLM benchmark site, with a companion post on r/BlackwellPerformance covering single-RTX Pro 6000 performance. It matters to practitioners choosing quantized models for local coding agents.
A community benchmark run compares the NVFP4-quantized Qwen3.8-Flash-Next against 27B-class and other local models on agentic coding tasks. Results are published on a public local-LLM benchmark site, with a companion post on r/BlackwellPerformance covering single-RTX Pro 6000 performance. It matters to practitioners choosing quantized models for local coding agents.
156 engagement2 sourcesredditFri, Aug 28, 2026, 05:30 PM