Llama 4 Maverick — Coding Scores
8.8/10
9.2/10
8.3/10
9.1/10
All scores from our benchmark testing ↓
In our 30-day testing, Llama 4 coding impressed most on large-codebase tasks. Its 1M token context window is a genuine differentiator — we fed it entire monorepos and received coherent refactoring suggestions spanning 40,000+ lines. That workflow is simply impossible with Qwen3’s 128K limit.
Where Llama 4 coding falls short: no dedicated thinking mode. For complex algorithmic challenges, it produces working-but-suboptimal solutions. We measured an 88% first-attempt compile rate across Python and TypeScript tasks our benchmark ↓ — solid, but not the best.
Llama 4 Maverick’s vision input is genuinely useful for UI coding. Our team converted a Figma mockup to working React components in under 35 seconds. That workflow doesn’t exist with any Qwen3 model currently.
- 1M token context — entire codebase fits in one prompt
- Native vision input — screenshot or Figma to code
- Scout variant: fastest lightweight self-hosted option
- Wide API ecosystem: Meta AI, AWS Bedrock, Groq, Fireworks
- Strong multilingual code support across major languages
- No thinking mode — struggles on hard algorithmic problems
- Llama Community License restricts use above 700M MAU
- Maverick’s 400B total params = expensive to self-host
- Lower first-attempt accuracy than Qwen3 in our tests
Qwen3 Coding Accuracy: The Numbers That Surprised Us
Qwen3-72B — Coding Scores
9.1/10
8.7/10
8.9/10
9.4/10
All scores from our benchmark testing ↓
Qwen3’s standout feature for developers is its hybrid thinking mode. For boilerplate and simple completions, it fires fast in non-thinking mode. Switch on thinking mode for complex tasks — implementing graph traversal, refactoring legacy callback hell, async Rust ownership patterns — and the quality gap over Llama 4 becomes obvious.
In our testing, Qwen3-72B produced correct, compilable code on the first attempt 91% of the time across 150 tasks our benchmark ↓. That 3-point edge over Llama 4 compounds fast in agentic pipelines where every failed attempt triggers an expensive retry loop.
Building a commercial coding product? Qwen3’s Apache 2.0 license means zero restrictions — no usage caps, no royalties, no legal review needed. The Llama Community License has a 700M MAU ceiling that could become a real concern at scale.
- Highest code accuracy in our tests — 91% first-attempt
- Hybrid thinking mode excels on hard algorithmic problems
- Apache 2.0 — unrestricted commercial use
- 8 model sizes from 0.6B to 235B — fits any budget or GPU
- Strong performance across 50+ programming languages
- No native vision input — text-only models
- 128K context limit vs Llama 4’s 1M tokens
- Thinking mode adds latency on complex prompts (~2-4s extra)
- Smaller Western developer community vs Meta’s ecosystem
Pricing & Deployment Costs Compared
Both models are open-weight — free to download and self-host. The real cost decision is managed API vs. your own GPU infrastructure. Here’s the 2026 pricing landscape for teams that want managed inference.
| Model | Input / 1M tokens | Output / 1M tokens | Provider |
|---|---|---|---|
| Llama 4 Scout | ~$0.11 | ~$0.34 | Meta AI API |
| Llama 4 Maverick | ~$0.19 | ~$0.65 | Meta AI API |
| Qwen3-72B | ~$0.88 | ~$0.88 | Together AI |
| Qwen3-235B-A22B | ~$1.20 | ~$1.20 | Alibaba Cloud |
| Both models (self-hosted) | $0 (weights free) | $0 (compute only) | Your infra (vLLM/Ollama) |
Pricing approximate as of April 2026. Verify current rates at (Meta AI) and (Together AI). Prices change frequently.
Pricing verdict: Llama 4 wins by a wide margin. At $0.19/1M input for Maverick, Meta is aggressively undercutting the market. For teams running coding agents that process millions of tokens daily, that’s an operational difference of thousands of dollars per month.
High-volume coding agent? Run Llama 4 Scout at $0.11/1M input for triage tasks, then route complex algorithmic work to Qwen3-72B. That hybrid routing strategy cuts costs 60–70% vs using Qwen3 exclusively, per our cost modeling our testing ↓.
Developer Use Cases: Which Model Fits Your Stack?
| Use Case | Best Model | Reason |
|---|---|---|
| Agentic coding pipelines | Qwen3-72B ✓ | 91% accuracy reduces costly re-prompts |
| UI screenshot → React code | Llama 4 Maverick ✓ | Only model with native vision input |
| Full-repo refactoring (40k+ lines) | Llama 4 Maverick ✓ | 1M token context — entire codebase fits |
| Competitive / algorithmic coding | Qwen3-72B ✓ | Thinking mode dominates hard problems |
| High-volume, cost-sensitive API | Llama 4 Scout ✓ | $0.11/1M — most affordable competitive option |
| Self-hosted on single GPU | Qwen3-8B / 14B ✓ | Runs on A10G/3090 with strong results |
| Commercial SaaS product | Qwen3 ✓ | Apache 2.0 — zero commercial restrictions |
After migrating three production coding pipelines between these models, our team found the use-case split to be refreshingly clean. There’s very little overlap in where each model truly excels.
Want more comparisons like this? Check our AI Tools reviews and Dev Productivity guides for more tested recommendations.
FAQ
Q: Is Llama 4 better than Qwen3 for coding overall?
Not overall. In our 30-day benchmark, Qwen3-72B hit 91% code accuracy vs Llama 4 Maverick’s 88%. Qwen3 also wins on licensing (Apache 2.0) and thinking-mode performance for complex algorithms. Llama 4 wins specifically for vision-to-code workflows and massive context windows (1M tokens). Which is “better” depends entirely on your use case. See our full methodology ↓
Q: Can I use Llama 4 or Qwen3 in a commercial product without licensing issues?
Qwen3 is Apache 2.0 — fully permissive for commercial use with zero restrictions. Llama 4 uses Meta’s Llama Community License, which restricts services with more than 700 million monthly active users. For virtually all startups and SaaS products this threshold is irrelevant, but it’s worth reading before you build. See the license at Meta Llama GitHub ↗.
Q: What GPU hardware do I need to self-host Qwen3-72B or Llama 4 Maverick?
Qwen3-72B at 4-bit quantization runs comfortably on a single A100 80GB GPU, or two A10G 24GBs in tandem. Llama 4 Maverick is a 400B total parameter MoE model — expect to need 4× H100s for smooth inference. For budget-conscious self-hosting, Qwen3-8B or 14B run on a single RTX 3090/4090 and still deliver strong coding results. Both support vLLM and Ollama.
Q: Does Qwen3 support Rust, Go, and TypeScript — not just Python?
Yes. In our 30-day testing across 250 tasks, Qwen3-72B performed strongly across Python, TypeScript, Rust, Go, Java, and C++. Rust and Go quality was notably better than Llama 4 Maverick on idiomatic patterns — ownership handling in Rust and goroutine patterns in Go were both more accurate on first attempt. Qwen3 officially supports 50+ programming languages per its model card on (Hugging Face ↗).
Q: What is the cheapest way to run Llama 4 coding tasks in production?
Llama 4 Scout via the Meta AI API at approximately $0.11/1M input tokens is the most cost-effective managed option. For even lower costs, run Scout on Groq (which offers extremely fast inference) or self-host if you have spare H100 capacity. If you need Maverick-level quality, the $0.19/1M input rate is still significantly cheaper than GPT-4o or Claude 3.5 Sonnet, making it the best coding AI value at that quality tier.
📊 Benchmark Methodology
| Metric | Qwen3-72B | Llama 4 Maverick |
|---|---|---|
| API Response Time (avg) | 1.1s | 1.4s |
| First-Attempt Compile Rate | 91% | 88% |
| Correct Logic (manual review) | 84% | 81% |
| Context Retention (10k+ tokens) | 8.7/10 | 9.2/10 |
| Debugging Accuracy | 89% | 83% |
| Vision → Code Quality | N/A | 9.1/10 |
Limitations: API latency reflects conditions during our testing window and varies by provider load. Results represent our specific environment and prompt style. Your accuracy numbers will vary based on prompt engineering and task domain.
Final Verdict: Best Llama 4 Coding vs Qwen3 for 2026
After 30 days of intensive production testing across 250 coding tasks, our verdict is clear: Qwen3-72B is the best coding AI for most developers in 2026. It leads on accuracy (91% vs 88%), licensing freedom (Apache 2.0), and thinking-mode quality for hard algorithmic problems — the factors that matter most in real production pipelines.
Llama 4 Maverick is not a weak alternative — it wins decisively in two scenarios: vision-to-code workflows and ultra-large context (1M token) analysis. If either defines your use case, Llama 4 is the correct choice, not a compromise.
| Team Profile | Recommended Model |
|---|---|
| Solo developer / AI coding assistant | Qwen3-72B ✓ |
| Startup building a coding SaaS product | Qwen3-72B ✓ (Apache 2.0) |
| Enterprise team, full-repo analysis | Llama 4 Maverick ✓ |
| Design-to-code (Figma / UI workflows) | Llama 4 Maverick ✓ |
| High-volume, cost-sensitive agent deployment | Llama 4 Scout ✓ |
| Limited GPU, self-hosted deployment | Qwen3-8B / 14B ✓ |
Both models are available free on (Hugging Face (Qwen)) and (Hugging Face (Meta Llama)). Start with the smallest model in your preferred family — Qwen3-8B or Llama 4 Scout — validate it against your actual workload, then scale up. Don’t pay for 235B parameters until you’ve confirmed the smaller model can’t do the job.
📚 Sources & References
- (Qwen on Hugging Face) — Official Qwen3 model cards, context window specs, license
- (Meta Llama on Hugging Face) — Official Llama 4 Scout and Maverick model cards
- QwenLM GitHub Organization — Open-source code, release notes, community
- Meta Llama GitHub Organization — Open-source code, Llama Community License
- (Meta AI — Official Llama Page) — API access and pricing
- Bytepulse 30-Day Benchmark — 250-task production test, March–April 2026 (methodology above)
- Provider Pricing Data — Together AI, Meta AI API, Alibaba Cloud (verified April 2026; rates subject to change)
We link only to verified GitHub organizations and official product pages. Pricing cited as text where direct pricing-page URLs may change frequently.
