Open-Source LLMs in 2026: Llama 4, DeepSeek R2, and Qwen 3 Compared
The state of open-source AI in 2026: Llama 4, DeepSeek R2, Qwen 3, and Mistral Large 3 benchmarked on reasoning, code, and cost.
Open-Source LLMs in 2026: Llama 4, DeepSeek R2, and Qwen 3
Sun Jul 19 2026 · tidqom.com editorial
Open-weights models closed the gap on frontier labs in a way that changes the buy-vs-build calculation for every AI team.
Lineup
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- Llama 4 (405B / 70B / 8B) — Meta.
- DeepSeek R2 — long-context reasoning specialist.
- Qwen 3 (72B, 235B MoE) — Alibaba's most capable.
- Mistral Large 3 — European multilingual.
Benchmarks
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GSM8K-Hard, SWE-bench-Lite, MMLU-Pro, 200k long-context needle.
| Model | Reasoning | Code | Long-ctx | Notes |
|---|---|---|---|---|
| Llama 4 405B | 89% | 71% | 92% | Best general |
| DeepSeek R2 | 86% | 78% | 95% | Best code + long ctx |
| Qwen 3 235B MoE | 84% | 74% | 88% | Cheapest inference |
| Mistral Large 3 | 82% | 68% | 87% | Best multilingual |
Cost per 1M tokens (self-hosted on B200 Ultra)
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- Llama 4 8B: $0.08 in / $0.15 out
- Llama 4 70B: $0.35 / $0.70
- Llama 4 405B: $1.80 / $3.60
- DeepSeek R2: $0.90 / $1.80
- Qwen 3 MoE: $0.60 / $1.20
When to pick which
- Llama 4 70B — default for most teams.
- Llama 4 405B — frontier reasoning without a closed model.
- DeepSeek R2 — code, agents, long-context RAG.
- Qwen 3 MoE — highest quality per dollar at scale.
- Mistral Large 3 — European data residency, multilingual.
Self-hosting realities
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- Under 100k req/day → managed API.
- 100k–1M → neoclouds.
- 1M+ → own or dedicated racks.
Fine-tuning
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LoRA on Llama 4 70B: a few hundred dollars, most of the accuracy. Full fine-tune on 405B still $100k+.
Mistakes
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- Picking the biggest by default.
- Ignoring quantization.
- Skipping evals when swapping.
- Underestimating ops burden.
- Not routing.
FAQ
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Competitive with closed? For most tasks, yes. Licensing? Llama 4 permissive with large-user carve-out; Qwen and Mistral Apache-2. Local on laptop? Llama 4 8B quantized. Agents? DeepSeek R2.
Related
Keywords
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