TL;DR — Which Model Wins for Developers in 2026?

The honest summary is that there’s no universal winner — the best choice depends on what you’re optimizing for. Gemma 4 wins on reasoning quality per parameter: the 31B model achieves benchmark results competitive with much larger models, making it the best choice for teams with consumer GPU constraints (24GB VRAM) who need strong math and coding. Llama 4 Scout wins on context window — 10 million tokens allows processing entire repositories or very long documents in a single pass, a capability no other open-weight model matches. Qwen 3 wins on raw coding performance at the 235B scale (73.4% SWE-bench Verified) and leads on AIME 2024 math benchmarks at 85.7%. Licensing matters: Gemma 4 uses Google’s Gemma license (permissive but not Apache 2.0), Llama 4 uses Meta’s custom Llama license (commercial use allowed above 700M MAU requires license), and Qwen 3 uses Apache 2.0 (most permissive). For most developer teams running local models on consumer hardware: Gemma 4 27B-IT is the recommended starting point. For production API deployments: Qwen3-235B-A22B or Llama 4 Maverick depending on cost and context requirements.

The Contenders: Gemma 4, Llama 4, and Qwen 3 at a Glance

All three families release multiple sizes, from 1-4B models for edge deployment to 100B+ for maximum capability. The MoE (Mixture of Experts) architecture used by Llama 4 and Qwen 3 allows large parameter counts with relatively lower inference cost — active parameter count matters more than total count for inference speed and VRAM requirements. Gemma 4 uses dense architecture, making per-token compute higher but more predictable.

Benchmark Showdown — Coding, Reasoning, and Math

Published benchmark results as of 2026:

Coding (SWE-bench Verified):

• Qwen3-235B-A22B: 73.4% (best open-weight coding model)
• Llama 4 Maverick: ~72% (comparable)
• Gemma 4 31B: ~65% (strong for its size)

Math (AIME 2026):

• Gemma 4 31B: 89.2% (330% improvement over Gemma 3)
• Qwen3-235B-A22B: 85.7% on AIME 2024
• Llama 4 Maverick: ~78%

Reasoning (GPQA Diamond):

• Qwen3-235B-A22B: 77.2% (strongest open-weight)
• Gemma 4 31B: ~72%
• Llama 4 Maverick: ~70%

Coding efficiency (LiveCodeBench v6):

• Gemma 4 31B: 80.0% (Codeforces ELO jumped from 110 to 1100+ vs Gemma 3)
• Qwen3-32B: ~74%
• Llama 4 Scout: ~68% (trade-off for context size)

General knowledge (MMLU):

• Llama 4 Maverick: 85.5%, beating GPT-4o on multiple benchmarks
• Qwen3-235B-A22B: ~84%
• Gemma 4 31B: ~82%

The practical interpretation: Qwen 3 wins on aggregate capability at full size. Gemma 4 wins on capability-per-parameter — the 31B model punches above its weight class. Llama 4 Scout trades raw benchmark performance for the 10M context window.

Architecture and Model Sizes Compared

Gemma 4 uses a dense transformer architecture across four sizes: 4B (edge deployment), 12B (mid-range), 27B (recommended for most teams), and 31B (maximum capability). The 27B and 31B models include vision capabilities — image understanding in the same model without separate vision tower. Google’s post-training methodology shows significant improvement, with RLHF and constitutional AI techniques producing stronger instruction-following than the raw pre-training benchmarks suggest.

Llama 4 introduces MoE architecture to the Llama family for the first time. Scout (17B active / 109B total, 16 experts) is optimized for the 10M token context window. Maverick (17B active / ~400B total, 128 experts) sacrifices the ultra-long context for stronger per-query reasoning. Both models include multimodal vision. The MoE design means Scout inference on local hardware requires significantly less VRAM than the parameter count suggests — 17B active parameters need ~24-28GB VRAM for float16.

Qwen 3 uses MoE for large models (235B-A22B) and dense for smaller ones (32B, 14B, 7B, 4B, 1.7B, 0.6B). The 235B-A22B model is the leading open-weight option for raw capability but requires data center hardware (multiple A100s). The 32B dense model is the practical choice for teams with high-end consumer hardware (2× RTX 4090 in NVLink or single A100-80GB). Qwen 3 models are text-only — no vision support — which matters for multimodal use cases.

Deployment Reality: Consumer GPU vs Data Center

Consumer GPU (24GB VRAM — RTX 3090, RTX 4090, RTX A5000):

• Gemma 4 27B: runs at Q4 quantization, good quality
• Llama 4 Scout: runs at Q4 (17B active parameters fits well)
• Qwen3-32B: requires Q3-Q4 quantization, tight
• Qwen3-235B-A22B: not possible on single consumer GPU

Professional workstation (2× 24GB or 48-80GB VRAM — A6000, A100-40GB):

• All models above in higher precision
• Qwen3-235B-A22B: requires multi-GPU (4× A100-40GB or 2× A100-80GB)

Ollama setup for any model:

# Gemma 4
ollama pull gemma4:27b

# Llama 4 Scout
ollama pull llama4:scout

# Qwen 3
ollama pull qwen3:32b    # consumer GPU
ollama pull qwen3:235b   # data center only

Speed at Q4 quantization on RTX 4090:

• Gemma 4 27B: ~35 tokens/second
• Llama 4 Scout: ~40 tokens/second (MoE efficiency)
• Qwen3-32B: ~30 tokens/second

Licensing Deep Dive — Apache 2.0 vs Llama License vs Gemma License

Licensing determines what you can build commercially:

Qwen 3 — Apache 2.0: Most permissive. Use, modify, distribute, embed in commercial products without restrictions. No source disclosure, no royalties, no usage limits. Best for teams building commercial products on top of the model.

Gemma 4 — Gemma License: Permissive for commercial use but not OSI-recognized open source. Prohibits use in certain application categories (weapons, surveillance). No modifications that bypass safety filters. Acceptable for most business use cases.

Llama 4 — Llama License: Commercial use allowed for companies with fewer than 700 million monthly active users. Above that threshold, a license from Meta is required. Derivatives must be released under the same Llama license. Acceptable for most teams; the 700M MAU threshold only affects very large consumer platforms.

For teams embedding LLMs in commercial SaaS products: Qwen 3 Apache 2.0 provides the cleanest legal path. For internal tools and direct API use, all three licenses are effectively equivalent in practice.

Context Window Comparison: 256K vs 1M vs 10M Tokens

The context window difference between these models is the most dramatic practical differentiator:

Gemma 4 27B: 256K tokens — sufficient for most coding tasks, long documents, and multi-file analysis. Cannot process entire large codebases in one pass.

Llama 4 Maverick: 1M tokens — allows processing complete medium-sized codebases, entire books, and extended conversations with full history.

Llama 4 Scout: 10M tokens — the largest context window in any open-weight model as of 2026. Enables feeding a 500,000-line codebase entirely into context, or processing hours of audio transcription, or analyzing a comprehensive document set without chunking or RAG.

Qwen3-235B-A22B: 128K tokens — the most limited among the flagship models, making it dependent on RAG or chunking for large document workflows.

The 10M context Scout is a category shift for specific use cases: full codebase understanding, very long document analysis, and multi-document reasoning where chunking introduces information loss. The trade-off is benchmark quality — Scout’s reasoning scores are lower than Maverick’s.

Real-World Developer Use Cases: Which Model for Which Task

The choice of model for each developer workflow depends primarily on three variables: available hardware, context window needs, and licensing requirements. For each major use case, the model family advantages map differently to practical outcomes.

Coding agent (autonomous multi-file editing): Qwen3-235B-A22B or Llama 4 Maverick — both have strong SWE-bench scores. Use Qwen for Apache 2.0 licensing; use Llama 4 Maverick for the larger context window when understanding cross-file relationships matters.

Local coding assistant with consumer GPU: Gemma 4 27B — best quality per VRAM usage, strong LiveCodeBench performance, runs well on single RTX 4090.

Large codebase analysis / document processing: Llama 4 Scout — the 10M context window is the only option for whole-codebase understanding without chunking.

Math and reasoning tasks: Gemma 4 31B on AIME 2026 (89.2%) — exceptional for its parameter count.

Commercial product embedding with clean licensing: Qwen 3 (Apache 2.0) — no license negotiation, no usage restrictions.

FAQ

What is the best open-source LLM for coding in 2026?

For raw coding performance at full scale: Qwen3-235B-A22B scores 73.4% on SWE-bench Verified, the highest among open-weight models. For practical coding on consumer hardware: Gemma 4 27B delivers strong LiveCodeBench results (80.0%) and runs on a single RTX 4090. Llama 4 Maverick is competitive (~72% SWE-bench) with the advantage of a 1M token context window for understanding larger codebases.

Which model has the largest context window?

Llama 4 Scout has the largest context window at 10 million tokens — the record among open-weight models as of 2026. This enables processing entire large codebases, books, or extensive document collections in a single pass without chunking. Llama 4 Maverick offers 1M tokens with stronger reasoning. Gemma 4 caps at 256K tokens.

Which license is most permissive for commercial use?

Qwen 3 uses Apache 2.0, the most permissive license: no restrictions on commercial use, no source disclosure requirements, no usage limits. Gemma 4’s Gemma License allows commercial use with category restrictions. Llama 4’s Llama License allows commercial use for companies under 700M monthly active users.

Can Gemma 4 run on a consumer GPU?

Yes. Gemma 4 27B runs on a single 24GB VRAM GPU (RTX 3090, RTX 4090) at Q4 quantization with approximately 35 tokens/second throughput. The 12B model runs on 16GB VRAM GPUs. The 4B model runs on 8GB. Gemma 4’s dense architecture makes VRAM requirements predictable.

Does Qwen 3 support multimodal inputs?

No. Qwen 3 models are text-only and do not support vision or audio inputs. For multimodal tasks requiring image understanding, Gemma 4 27B and larger offer vision capabilities, as do both Llama 4 Scout and Maverick. This is a notable gap in Qwen 3’s capability profile for developers building multimodal applications.