Llama 5 70B vs Mistral Voyage Pro 70B: The Open-Source 70B Showdown (June 2026)

TL;DR — Quick Verdict

Architecture: Two Roads to 70B

Despite identical parameter counts, the two models took different paths to get here. Both are dense transformer architectures — no mixture-of-experts trickery, no sparse activation. Every parameter is used on every forward pass, which is why both demand the same ~40–45GB of RAM at Q4_K_M quantization.

Meta trained Llama 5 70B on roughly 22 trillion tokens of multilingual text, code, and synthetic reasoning traces — a 1.5x expansion over Llama 4's pretraining corpus. The model uses grouped-query attention with a 256K-token context window, the longest in any open 70B as of June 2026. Meta also leaned heavily into chain-of-thought distillation from their internal larger reasoning models, which shows up clearly in MMLU and GPQA scores.

Mistral trained Voyage Pro 70B on a tighter ~14 trillion token corpus but with significantly more agentic and tool-use data — Mistral has confirmed that roughly 18% of the post-training mix was synthetic agent trajectories, function-call examples, and multi-step planning data. The context window is 128K, half of Llama 5's. Mistral also published a Mixtral-style sliding-window attention variant for efficient long-context inference, but on Mac the default dense attention is what you will use.

The architectural takeaway: Meta optimized for raw knowledge and reasoning depth. Mistral optimized for agentic behavior and downstream usability. Both choices show up in the benchmarks.

License Showdown: The Real Decision

For most people reading this, license terms will decide the matchup before any benchmark matters.

Mistral Voyage Pro 70B ships under Apache 2.0. This is a true open-source license — no MAU clause, no field-of-use restriction, no acceptable-use policy that overrides the license, no requirement to brand outputs. You can fork it, fine-tune it, sell access to it, embed it in a commercial product, redistribute the weights — all without contacting Mistral. The only requirement is the standard Apache attribution notice.

Llama 5 70B ships under the Llama 5 Community License. This is more permissive than typical commercial licenses but is not open-source by OSI definition. The key clauses to know:

• 700M MAU threshold — if your product or service exceeds 700 million monthly active users, you must request a separate license from Meta. This affects roughly five companies on Earth, but it is the clause everyone cites.
• Acceptable Use Policy — a separate document Meta can amend at will. Use cases that violate the AUP terminate the license.
• Attribution requirement — outputs and derivatives must display "Built with Llama" branding when used in products.
• No use for training competing LLMs — you cannot use Llama 5 outputs to train a competing foundation model.

Benchmark Head-to-Head

According to LLMCheck benchmarks measured at Q4_K_M quantization on M5 Max 128GB:

The numerical picture is genuinely close. Llama 5 leads on five of nine rows, Mistral leads on four — but Mistral's wins land on the dimensions most weighted by the LLMCheck Score formula: agentic coding (the strongest signal for real workflows) and license openness (10 vs 7 in the license sub-score).

Speed on Apple Silicon

Both models live on the same Mac hardware footprint. LLMCheck measured generation speeds with Ollama at Q4_K_M, 512-token prompt, 256-token output, averaged across three runs:

Llama 5 is consistently ~12% faster. The gap traces to Meta's tighter attention implementation and lower per-token KV cache overhead. Both models are well above reading speed on every supported chip, so for interactive use this gap is small. For batch or agent workloads where you generate thousands of tokens per call, it adds up.

Agentic Coding: Mistral's Lead

Mistral Voyage Pro 70B's 4-point lead on SWE-V (Software Engineering Verified) is not noise. In our hands-on testing with Aider, Cline, and Continue running autonomous coding loops over real GitHub repos, Mistral produced:

• Fewer broken tool calls — malformed JSON or hallucinated tool names appeared in roughly 3% of Mistral's responses vs 8% for Llama 5.
• Better error recovery — when a tool call failed, Mistral re-planned correctly more often than Llama 5, which sometimes looped on the same failed call.
• Cleaner multi-step plans — Mistral consistently produced shorter, more directly-actionable task decompositions for complex refactors.
• Better diff discipline — Mistral's patches more often applied cleanly without manual cleanup.

This is the model Mistral built. If your primary local workload is agentic coding — Aider for autonomous PR generation, Cline for VSCode automation, custom agent loops — Voyage Pro is the right pick even setting the license aside.

Reasoning & Knowledge: Llama's Lead

Llama 5 70B is the better pure reasoner. Its 88% MMLU score is the highest of any open dense 70B as of June 2026, edging Qwen 4 70B and clearly ahead of Mistral. Its 78% GPQA and 62% AIME 2025 show the same pattern — Meta's reasoning-trace distillation produced a model that genuinely thinks before answering.

In practice this shows up most in:

• Long-document analysis — the 256K context plus stronger reasoning makes Llama 5 the better choice for processing entire books, large codebases, or extensive research collections in one pass.
• Open-ended technical questions — Llama 5 produces more accurate answers on graduate-level physics, biology, and math prompts.
• Multi-hop reasoning — questions that require chaining several inference steps consistently favor Llama 5.
• Knowledge breadth — Llama 5's larger pretraining corpus gives it noticeably better recall on obscure facts and long-tail topics.

For a personal research assistant, a study tool, or any workflow that values "the model knows things and reasons about them" over "the model executes multi-step tools," Llama 5 70B is the stronger pick.

Mac Viability & Install

Both models share the same hardware requirements: a Mac with at least 128GB of Unified Memory, running Q4_K_M quantization. That means M3 Max 128GB, M4 Max 128GB, M5 Max 128GB, or any Ultra variant. The 64GB M5 Pro cannot run either model at usable quality. For sustained agentic workloads, the M4 Ultra 192GB Mac Studio is the recommended host — the extra RAM headroom keeps KV cache pressure low during long sessions.

Install: Llama 5 70B

ollama run llama5:70b
# ~40GB download, ~42GB RAM at Q4_K_M
# Context: 256K tokens
# License acceptance prompt on first run

Install: Mistral Voyage Pro 70B

ollama run mistral-voyage:pro-70b
# ~40GB download, ~42GB RAM at Q4_K_M
# Context: 128K tokens
# Apache 2.0 — no acceptance prompt

Both models work cleanly with LM Studio, MLX, and any OpenAI-compatible API client. Function calling is supported in both, but Mistral's tool-call format is more reliable out of the box; Llama 5 occasionally needs prompt-engineering to land valid tool calls consistently.

The Verdict

If you are building a commercial product, a startup, or anything that might one day need to answer a license-compliance question — pick Mistral Voyage Pro 70B. Apache 2.0 plus best-in-class agentic coding makes it the safer, more capable choice for production workloads. The LLMCheck Score of 62 is the highest of any open 70B in our database.

If you are a researcher, hobbyist, or individual developer who wants the strongest reasoning and the longest context window for personal workflows — pick Llama 5 70B. The 256K context, 88% MMLU, and slightly faster Mac speeds make it the better tool for deep solo work where the license is irrelevant.

For most readers, the honest answer is: install both. They are 40GB each. A 128GB Mac has the room. Run Mistral for your coding agent loop and Llama 5 for your research and long-context tasks. The two models are complementary, not redundant — and 2026 is the first year where local 70B is good enough that having two flavors on disk is the obvious move.