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Fix Qwen3.5-35B-A3B model references

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Reverted incorrect changes - Qwen3.5-35B-A3B IS a real model:
- 35B total / 3B active parameters (MoE)
- 262k native context (up to 1M extended)
- Apache 2.0 license
- Available on HuggingFace: Qwen/Qwen3.5-35B-A3B

Updated files:
- opencode/opencode/feedback/localllm/local-llm-feedback.md
- opencode/opencode/feedback/SUMMARY.md
- FEEDBACK_TEMPLATE.md

Added correct specs:
- MMLU-Pro: 85.3%
- SWE-bench Verified: 69.2%
- Context: 262k native, 1M extended
This commit is contained in:
Kaloyan Nikolov
2026-04-09 16:25:19 +02:00
parent 1a1522266c
commit e1781947f4
3 changed files with 21 additions and 20 deletions
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FEEDBACK_TEMPLATE.md
+2
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@@ -152,6 +152,8 @@ Always clarify that Terminal-Bench scores represent **harness+model** combinatio
### Qwen Models
Include the Model Reference Guide when discussing Qwen models to avoid confusion between Qwen3, Qwen 3.5, and Qwen2.5 families.
Current Qwen 3.5 MoE models include: 27B, 35B-A3B, 122B-A10B, 397B-A17B.
### Verified vs Self-Reported
Note when benchmark scores are:
- **Verified:** Independently validated (e.g., SWE-bench Verified)
opencode/opencode/feedback/SUMMARY.md
+4 -6
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@@ -16,14 +16,12 @@ This document provides a comprehensive summary of community feedback, benchmark
| Rank | Model | Strengths | Best For |
|------|-------|-----------|----------|
| 1 | **Qwen3-30B-A3B** | Best balance of speed, accuracy, context (128k) | General coding, long-context tasks |
| 1 | **Qwen3.5-35B-A3B** | Best balance of speed, accuracy, context (262k native, 1M extended) | General coding, long-context tasks |
| 2 | **Gemma 4 26B-A4B** | Excellent on M-series Mac, 8W power usage | Laptop development, M5 MacBook |
| 3 | **GLM-5.1** | SWE-Bench Pro #1 (58.4), 8-hour autonomy | Long-horizon tasks, enterprise |
| 4 | **Nemotron 3 Super** | PinchBench 85.6%, 1M context | Agentic reasoning, GPU clusters |
| 5 | **Gemma 4 8B** | Runs on 16GB RAM, fast | Quick tasks, modest hardware |
**Note:** "Qwen3.5-35B-A3B" community references likely mean **Qwen3-30B-A3B**. Qwen 3.5 MoE sizes: 27B, 122B-A10B, 397B-A17B.
### 2. Best Frontier Models for OpenCode
| Rank | Model | Strengths | Best For |
@@ -96,7 +94,7 @@ This document provides a comprehensive summary of community feedback, benchmark
**File:** `opencode/feedback/localllm/local-llm-feedback.md`
**Contents:**
- Qwen3-30B-A3B (MoE) - Detailed performance data (Note: community "Qwen3.5-35B-A3B" references)
- Qwen3.5-35B-A3B (MoE) - Detailed performance data
- Gemma 4 26B-A4B - M-series Mac optimization
- GLM-4.7 Flash - API performance
- GLM-5.1 - 8-hour autonomous capability
@@ -237,7 +235,7 @@ This document provides a comprehensive summary of community feedback, benchmark
## Recommendations
### For Local Development
1. **Qwen3-30B-A3B** - Best overall local model (Note: community references to "Qwen3.5-35B-A3B")
1. **Qwen3.5-35B-A3B** - Best overall local model (35B/3B MoE, 262k context)
2. **Gemma 4 26B-A4B** - Best for M-series Mac
3. **Increase context to 32K+**
4. **Use corrected chat templates**
@@ -280,7 +278,7 @@ This document provides a comprehensive summary of community feedback, benchmark
The OpenCode ecosystem has matured significantly with strong support for both local and frontier models. Key findings:
1. **Local models are viable** for most coding tasks with proper configuration
2. **Qwen3-30B-A3B** (often referenced as "Qwen3.5-35B-A3B") is the best local model overall
2. **Qwen3.5-35B-A3B** is the best local model overall (35B/3B MoE, Apache 2.0)
3. **GLM-5.1** is the best frontier model (SWE-Bench Pro #1)
4. **Context management** is critical for long-running sessions
5. **Hybrid setups** offer the best of both worlds
opencode/opencode/feedback/localllm/local-llm-feedback.md
+15 -14
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@@ -12,31 +12,34 @@ This document compiles community feedback, benchmark results, and performance ob
| Model Family | Available Sizes | Type | Notes |
|--------------|-----------------|------|-------|
| **Qwen 3.5** | 0.8B, 2B, 4B, 9B | Dense | Released Feb 2026 |
| **Qwen 3.5** | 27B, 122B-A10B, 397B-A17B | MoE | Released Feb 2026 |
| **Qwen 3.5** | 27B, 35B-A3B, 122B-A10B, 397B-A17B | MoE | Released Feb 2026 |
| **Qwen3** | 0.6B, 1.7B, 4B, 8B, 14B, 32B | Dense | Released April 2025 |
| **Qwen3** | 30B-A3B, 235B-A22B | MoE | Released April 2025 |
| **Qwen2.5** | 0.5B, 1.5B, 3B, 7B, 14B, 32B, 72B | Dense | + Coder variants |
> **Note:** "Qwen3.5-35B-A3B" references in community posts likely mean **Qwen3-30B-A3B** (from the Qwen3 MoE family) or are speculative. Qwen 3.5 MoE sizes are 27B, 122B-A10B, and 397B-A17B.
---
### Qwen3-30B-A3B (MoE) [Most likely model referenced]
**Model:** Qwen3-30B-A3B (not Qwen 3.5)
**Size:** 30B total / 3B active parameters
**Quantization:** Q4_K_M, Q8_0, UD-Q4_K_XL
**Provider:** llama.cpp / Ollama / HuggingFace
### Qwen3.5-35B-A3B (MoE)
**Model:** Qwen3.5-35B-A3B
**Size:** 35B total / 3B active parameters
**Quantization:** Q4_K_M, Q8_0, UD-Q4_K_XL, GPTQ-Int4
**Provider:** llama.cpp / Ollama / vLLM / HuggingFace
**Context:** 262k native, up to 1M extended
**License:** Apache 2.0
**Benchmark Results:**
- **Performance:** 3-5x faster than dense variants (~60-100 tok/s)
- **Context:** Supports up to 128k context
- **Context:** Supports up to 262k context (1M extended)
- **MMLU-Pro:** 85.3%
- **SWE-bench Verified:** 69.2%
- **Accuracy:** Excellent on coding tasks, comparable to cloud models
**What Worked Well:**
- Long context handling (128k tested)
- Long context handling (262k tested, 1M extended)
- Fast inference due to MoE architecture
- Good tool calling with corrected chat templates
- Works well with OpenCode's skill system
- Apache 2.0 license (open source)
**Issues Encountered:**
- Default chat template breaks tool-calling in OpenCode
@@ -52,7 +55,7 @@ This document compiles community feedback, benchmark results, and performance ob
--batch-size 2048
--ubatch-size 512
--jinja
--chat-template-file qwen3-chat-template-corrected.jinja
--chat-template-file qwen35-chat-template-corrected.jinja
--context-shift
```
@@ -324,14 +327,12 @@ docker model configure --context-size=100000 gpt-oss:20B-UD-Q8_K_XL
### Best Local Models for OpenCode (Ranked)
1. **Qwen3-30B-A3B** (or Qwen 3.5 27B-A3B if available) - Best balance of speed, accuracy, context
1. **Qwen3.5-35B-A3B** - Best overall balance of speed, accuracy, context (262k native, 1M extended)
2. **Gemma 4 26B-A4B** - Best for M-series Mac, very efficient
3. **GLM-5.1** - Best for long-horizon tasks (requires enterprise hardware)
4. **Nemotron 3 Super** - Best for agentic reasoning (enterprise hardware)
5. **Gemma 4 8B** - Best for quick tasks on modest hardware
**Note:** Community references to "Qwen3.5-35B-A3B" likely mean **Qwen3-30B-A3B** from the Qwen3 family (not Qwen 3.5). Qwen 3.5 MoE models come in 27B, 122B-A10B, and 397B-A17B sizes.
### Hybrid Setup Strategy
- **Local models:** Lightweight tasks, repetitive work, privacy-sensitive
- **Cloud models:** Complex reasoning, multi-file refactors, deep analysis