Open source AI models are beating paid models now. GLM-5.1 scored #1 on SWE-Bench Pro, ahead of Claude Opus 4.6 and GPT-5.4. And it's free under MIT license.
As of April 2026, four open-source LLMs stand out. Here's how they compare.
Quick Comparison
| Llama 4 Maverick | Gemma 4 | DeepSeek V4 | GLM-5.1 | |
|---|---|---|---|---|
| Developer | Meta | DeepSeek | Z.ai (Zhipu) | |
| Parameters | 400B (17B active) | 26B (3.8B active) | ~1T (37B active) | 744B (40B active) |
| Context | 1M tokens | 256K | 1M tokens | — |
| License | Llama License | Apache 2.0 | MIT | MIT |
| Local Run | Difficult | 18GB RAM | Difficult | Difficult |
| API Price | $0.19/M | Free (local) | $0.30/M | Subscription |
All four use MoE (Mixture of Experts) architecture — like a buffet that prepares every dish but serves each guest only what they need.
Llama 4 Maverick — The 1M Token Giant
400B total parameters with 128 experts, but only 17B active per inference. The biggest weapon is the 1 million token context window — the widest among open-source models.
MMLU 85.5%, highest among open models. But the Llama License isn't fully open source — if your service has 700M+ MAU, you need separate permission from Meta.
Gemma 4 — Frontier AI on Your Laptop
Google's 26B MoE model runs locally with just 18GB RAM. Install via Ollama in 5 minutes. Your data never leaves your machine.
Apache 2.0 — the most permissive license. No MAU caps, no restrictions, full commercial freedom. 140+ languages supported. MMLU Pro 85.2%, Arena AI #3 among open models.
DeepSeek V4 — 1/50th the Price
~1 trillion parameters, SWE-bench Verified 81%. API pricing: $0.30 input, $0.50 output per million tokens. That's roughly 1/50th of GPT-5.4's cost at ~90% quality.
1M token context with 97% Needle-in-a-Haystack accuracy. MIT license. If cost matters, this is the answer.
GLM-5.1 — Coding Benchmark Champion
Z.ai (formerly Zhipu AI) released this on April 7, 2026. SWE-Bench Pro: 58.4 — #1, beating Claude Opus 4.6 and GPT-5.4. First open-source model to top this benchmark.
The standout feature: 8-hour autonomous coding. It can work on a single coding task for up to 8 hours without human intervention. MIT license, weights on Hugging Face.
When to Use What
| Situation | Pick | Why |
|---|---|---|
| Coding automation | GLM-5.1 | SWE-Bench Pro #1, 8h autonomous |
| API service at scale | DeepSeek V4 | 1/50th GPT price, 90% quality |
| Local / offline AI | Gemma 4 | 18GB RAM, Ollama, 5 min setup |
| Large document processing | Llama 4 | 1M tokens, MMLU 85.5% |
| License freedom | Gemma 4 / DeepSeek | Apache 2.0 / MIT |
No single winner. The right choice depends on your use case. All four are free or cheap enough to try — no reason to pick just one.
Originally published at GoCodeLab. Benchmarks as of April 2026.
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