Qwen3-Coder-Next 80B Gemma4-26B-A4B MoE MXFP4 Coding Benchmark Local LLM
🔍 The Bottom Line: Qwen3-Coder 8.7 vs Gemma4 8.3
We ran two powerful local LLMs simultaneously in an NVIDIA DGX Spark environment: Qwen3-Coder-Next 80B and Gemma4-26B-A4B. Both models utilize MoE (Mixture of Experts) architecture and MXFP4 quantization, allowing you to use them without any cost burden.
However, when it comes to the question “Which model should I use for which task?”, we need a clear answer. That’s why we conducted a benchmark across 5 key categories, ranging from code generation to refactoring. Ultimately, while Qwen3-Coder led with an average score of 8.7 compared to Gemma4’s 8.3, their individual characteristics were completely different.
📋 Test Environment and Model Specs
The core hardware for this test is the GB10 Grace Blackwell superchip from the DGX Spark. This chip features 128GB of unified memory, allowing the CPU and GPU to share memory efficiently. Thanks to this, you can load large models instantly without VRAM bottlenecks.
| Item | Qwen3-Coder-Next | Gemma4-26B-A4B |
|---|---|---|
| Total Parameters | 80B | 26B |
| Active Parameters | 3B (3.75%) | 3.8B (14.6%) |
| Architecture | MoE (Sparse) | MoE (Sparse) |
| Quantization | MXFP4 MOE (GGUF) | MXFP4 MOE (GGUF) |
| Context Window | 200K | 200K |
| Inference Cost | Free (Local) | Free (Local) |
| Specialization | Coding | General Purpose |
MoE is a technology that activates only a small number of “Expert” layers required for a specific task out of the total parameters. Qwen3-Coder uses only 3B out of 80B, while Gemma4 uses 3.8B out of 26B. This allows you to achieve the performance of a large model while reducing the burden on memory bandwidth.
Since both models have fewer than 4B active parameters, inference speeds are extremely fast. In particular, research shows that Qwen3-Coder-Next can demonstrate up to 10x higher throughput relative to its active parameters.
🛠️ Detailed Results of 5 Tests
Test 1: Code Generation — Thread-safe LRU Cache
The first test was a request to “Implement a Thread-safe LRU Cache with O(1) time complexity.” We checked how accurately the models could generate logic and types.
| Metric | Qwen3-Coder | Gemma4 |
|---|---|---|
| Generic Typing | Generic[T] (Values only) | Generic[K, V] (Key + Value) |
| Input Validation | isinstance + Positive check | Positive check only |
| Additional Methods | delete, contains, len, repr | size, clear, repr |
| Test Inclusion | Usage examples only | assertions + thread safety tests |
| Tokens (prompt/completion) | 48 / 1,610 | 55 / 1,631 |
| Score | 8.0 | 9.0 ✓ |
Gemma4 won this test. This was because it provided a sophisticated design using Generic[K, V] along with an executable test suite. For tasks involving writing code from scratch, Gemma4’s habit of using type hints is a major strength.
Test 2: Bug Detection — Analysis of 3 Functions
The second test involved analyzing three functions with intentional bugs (Merge Sort, Binary Search, and Dictionary Flattening). We evaluated how meticulously the models read the code.
| Metric | Qwen3-Coder | Gemma4 |
|---|---|---|
| merge_sorted_lists bug | Detected (missing tail) | Detected |
| binary_search bug (3 bugs) | All detected + execution trace | All detected |
| flatten_dict evaluation | “No bug” (Correct) | False Positive ⚠️ |
| Depth of Analysis | Step-by-step execution trace | Clear but included misjudgment |
| Tokens (prompt/completion) | 263 / 1,742 | 323 / 1,200 |
| Score | 10.0 ✓ | 7.0 |
flatten_dict function as having a “bug.” It claimed the recursive call would fail, but in reality, it works correctly according to Python’s iteration rules. Such misjudgments can cause developers to waste unnecessary debugging time.
As a result, Qwen3-Coder won by a landslide with a perfect score. Accuracy in finding logical flaws in code is an uncompromisingly important factor.
Test 3: Algorithm — Merge Overlapping Intervals
The third test was an implementation test for an algorithm that merges overlapping intervals. We compared efficient logic and implementation styles.
| Metric | Qwen3-Coder | Gemma4 |
|---|---|---|
| Implementation Style | current_start/end variables | Directly modifying merged[-1] (Pythonic) |
| Type Hints | None | List[List[int]] |
| Complexity Analysis | Accurate | Accurate + LaTeX notation |
| Edge Cases | 4 specified | Brief |
| Score | 8.0 | 8.5 ✓ |
Gemma4 had a slight edge here. It provided a more Pythonic code style and clear type hints. However, Qwen3-Coder was excellent in terms of defensive programming by meticulously pointing out four different edge cases.
Test 4: Code Explanation — Run-Length Encoding
The fourth test evaluated the ability to look at code for a specific algorithm (RLE) and explain it to a user.
| Metric | Qwen3-Coder | Gemma4 |
|---|---|---|
| Core Concept Identification | Identified RLE immediately | Identified RLE immediately |
| Depth of Explanation | Detailed, mentioned limitations | Clean, included summary table |
| Precautions | “Only consecutive characters”, “Assuming strings” | Basic |
| Score | 9.0 ✓ | 8.0 |
Qwen3-Coder was superior in its ability to deeply understand and explain code. Its approach of proactively pointing out the algorithm’s limitations and precautions is extremely useful in professional documentation work.
Test 5: Refactoring — Nested Conditionals + Selection Sort
The final test was a refactoring test to improve existing complex code to be cleaner.
| Metric | Qwen3-Coder | Gemma4 |
|---|---|---|
| Core Refactoring | TYPE_RULES dict + sorted() | TYPE_CONFIG dict + sorted() |
| Guard Clause Strategy | unknown type → continue | inactive FIRST → continue (more efficient) |
| Type Hints | None | List[Dict[str, Any]] |
| Config Placement | Inside function | Module level (better practice) |
| Score | 8.5 | 9.0 ✓ |
Gemma4 won the refactoring test. It showed better structural design sense, such as efficiently arranging the order of guard clauses and moving configurations to the module level.
📊 Overall Analysis: What the Numbers Say
Final Scorecard
| Test Category | Qwen3-Coder | Gemma4 | Winner |
|---|---|---|---|
| 1. Code Generation | 8.0 | 9.0 | Gemma4 |
| 2. Bug Detection | 10.0 | 7.0 | Qwen3-Coder |
| 3. Algorithm | 8.0 | 8.5 | Gemma4 |
| 4. Code Explanation | 9.0 | 8.0 | Qwen3-Coder |
| 5. Refactoring | 8.5 | 9.0 | Gemma4 |
| Average Score | 8.7 | 8.3 | Qwen3-Coder |
Although Gemma4 won 3 out of 5 rounds, Qwen3-Coder led in the average score. This is due to the significant score difference in the bug detection category. Qwen3-Coder showed overwhelming stability in analytical tasks where accuracy is essential.
Token Usage Comparison
| Test Category | Qwen3-Coder (Total) | Gemma4 (Total) |
|---|---|---|
| 1. Code Generation | 1,658 | 1,686 |
| 2. Bug Detection | 2,005 | 1,523 |
| 3. Algorithm | 715 | 1,040 |
| 4. Code Explanation | 1,027 | 891 |
| 5. Refactoring | 1,104 | 1,246 |
| Total | 6,649 | 6,615 |
Token usage is nearly identical for both models. Since you are using them for free in a local environment, you can view token counts as a reference metric that doesn’t directly impact performance.
Comparison with Official Benchmarks
We checked whether our local test results align with official benchmark data.
Metrics related to Qwen3-Coder-Next:
| Benchmark | Qwen3-Coder-Next | Comparison | Source |
|---|---|---|---|
| SWE-Bench Verified | 70.6 | DeepSeek-V3.2 (70.2) | HuggingFace |
| SWE-Bench Pro | 44.3% | GLM-4.7 (40.6%) | VentureBeat |
| SecCodeBench | 61.2% | Claude Opus 4.5 (52.5%) | WinBuzzer |
Metrics related to Gemma4-26B:
| Benchmark | Gemma4-26B | Comparison | Source |
|---|---|---|---|
| LiveCodeBench v6 | 77.1% | Gemma 3 27B (29.1%) | Gemma4 Wiki |
| AIME 2026 | 88.3% | Based on 3.8B active params | Google Blog |
| Arena AI Text | #6 | Competing with 20x larger models | Google Blog |
The pattern where Qwen3-Coder is strong in secure coding and complex analysis, while Gemma4 excels in coding competitions and mathematical reasoning, aligns with the official data.
🎯 Routing Guide by Use Case
Here is a practical usage guide based on the test results. Use this to decide which model to call for specific tasks.
| Use Case | Recommended Model | Reasoning |
|---|---|---|
| Code Analysis / Bug Detection | Qwen3-Coder | Zero false positives, provides detailed execution traces |
| Code Generation / Scaffolding | Gemma4 | Excellent type hints and Pythonic style |
| Code Explanation / Documentation | Qwen3-Coder | Clearly identifies edge cases and limitations |
| Refactoring | Gemma4 | Structural design sense and application of OCP principles |
| Simple Information Extraction | Both | Minimal difference in lightweight tasks |
| Default Routing | Qwen3-Coder | Overall advantage in average accuracy |
📚 References
- Qwen3-Coder-Next Model Card — HuggingFace (2026)
- Gemma 4: Byte for byte, the most capable open models — Google Blog (2026)
- How DGX Spark’s Performance Enables Intensive AI Tasks — NVIDIA Technical Blog
- Qwen3-Coder-Next: Ultra-Sparse MoE Coding Model — VentureBeat (2026)
- Gemma 4 Coding Performance Benchmarks — Gemma4 Wiki (2026)
- Performance of llama.cpp on DGX Spark — llama.cpp Discussion
- Gemma 4 Day-1 Inference on DGX Spark — NVIDIA Developer Forums
- Related Post: Setting up a llama.cpp dual server on DGX Spark
✅ In Summary
Qwen3-Coder-Next and Gemma4-26B are complementary models with different strengths. Qwen3-Coder shows overwhelming performance in “reading and analyzing” code, while Gemma4 shows a superior sense for “writing and structuring” new code.
The optimal strategy is to run both models simultaneously within the 128GB memory of the DGX Spark and use them appropriately according to the nature of the task. Thanks to the MoE architecture, there is plenty of memory headroom, and you can enjoy the performance of top-tier models without any API costs. The era of local LLMs is truly right around the corner!