Benjamin Marie writes that while Qwen3.8 27B demonstrates superior accuracy across various tasks compared to the recently released Muse Glimmer—particularly in long-horizon agentic coding—Muse Glimmer offers significant advantages in memory efficiency due to its lower KV-cache consumption and shorter reasoning traces.
- Muse Glimmer's KV-cache uses approximately 4 times less memory than Qwen3.8.
- Qwen3.8 was evaluated specifically using xhigh thinking mode.
- The study examines the trade-offs between raw accuracy, token efficiency, and memory use.
- Muse Glimmer's KV-cache uses approximately 4 times less memory than Qwen3.8.
- Qwen3.8 was evaluated specifically using xhigh thinking mode.
- The study examines the trade-offs between raw accuracy, token efficiency, and memory use.
Benjamin Marie explores the trade-offs between accuracy and token efficiency when adjusting the reasoning effort settings in Qwen3.8 27B. By comparing configurations where thinking is disabled, set to low, medium, or xhigh, he examines whether increasing a model's "thinking" time provides significant performance gains relative to the added computational cost and memory usage.
- The study focuses on non-agentic tasks where prompts are evaluated as standalone problems.
- Higher reasoning effort can lead to significantly longer reasoning traces and increased generation time.
- Experiments were conducted using RTX Pro 6000 GPUs provided by Verda.
- The study focuses on non-agentic tasks where prompts are evaluated as standalone problems.
- Higher reasoning effort can lead to significantly longer reasoning traces and increased generation time.
- Experiments were conducted using RTX Pro 6000 GPUs provided by Verda.
Benjamin Marie writes that the effectiveness of an LLM in long-horizon agentic coding tasks depends heavily on the harness used to drive it rather than just the model itself. Through testing Qwen3.8 27B across three different interfaces—Mini-SWE Agent, Claude Code, and Pi—the author found that while specific configurations like "benchmaxxed" Pi can solve the highest number of tasks, other setups like Claude Code achieve better functional coverage (F2P). The study highlights how critical engineering choices, such as preserving reasoning traces or managing output token limits, are essential for successful agentic performance.
- The evaluation used DeepSWE 1.1, a benchmark comprising 113 long-horizon tasks from 91 open-source repositories.
- Performance varies significantly based on whether reasoning traces are preserved between turns and how context budgets are managed.
- Pi at medium effort was found to offer the best balance of efficiency and accuracy.
- Results were influenced by factors like session recovery, patch reliability, and output-token settings. author »
- The evaluation used DeepSWE 1.1, a benchmark comprising 113 long-horizon tasks from 91 open-source repositories.
- Performance varies significantly based on whether reasoning traces are preserved between turns and how context budgets are managed.
- Pi at medium effort was found to offer the best balance of efficiency and accuracy.
- Results were influenced by factors like session recovery, patch reliability, and output-token settings. author »
