Aya 23 35B vs Mistral Nemo 12B — Multilingual Match-Up
Last updated 2026-07-06
One model was built for 23 languages, the other for 128K of context on modest hardware. Here is which multilingual local LLM actually wins your VRAM.
By Mohamed Meguedmi · 9 min read
Key Takeaways
- Aya 23 35B wins on multilingual depth — it was purpose-built by Cohere For AI across 23 languages and posts the strongest low-resource-language accuracy of the two, but it costs roughly 3x the VRAM.
- Mistral Nemo 12B wins on practicality — a 128K context window, Apache 2.0 licensing, and ~7 GB at Q4_K_M mean it runs on a single 12 GB GPU that Aya 35B cannot touch.
- Context gap is enormous: Nemo's 128K vs Aya 23's 8K. For long documents, RAG, or code, this alone can decide the match.
- Licensing matters: Aya 23 ships under CC-BY-NC (non-commercial); Mistral Nemo is Apache 2.0 and free for commercial use.
- Verdict: Choose Aya 23 35B when translation and low-resource multilingual quality are the product; choose Mistral Nemo 12B for everything else, especially long-context and commercial workloads.
The two contenders at a glance
These models were released eight weeks apart in mid-2024 and answer very different questions. Aya 23 35B is Cohere For AI's answer to "how good can multilingual generation get if you concentrate capacity on 23 languages instead of spreading it across 100+?" Mistral Nemo 12B is Mistral AI and NVIDIA's answer to "what is the best general model that still fits comfortably on consumer hardware with a huge context window?"
The comparison in the SERPs is muddled — some pages confuse Mistral Nemo (the 12B open model) with the newer Mistral Nemotron. This guide is strictly about Mistral Nemo 12B, the July 2024 open-weight release under Apache 2.0.
| Spec | Aya 23 35B | Mistral Nemo 12B |
|---|---|---|
| Developer | Cohere For AI (C4AI) | Mistral AI + NVIDIA |
| Parameters | 35B | 12B |
| Base architecture | Command family (decoder) | Mistral / NeMo (decoder) |
| Context window | 8K (8,192 tokens) | 128K (131,072 tokens) |
| Languages (official) | 23 | 11 |
| Tokenizer | Command BPE | Tekken (Tiktoken-based) |
| License | CC-BY-NC 4.0 (non-commercial) | Apache 2.0 (commercial OK) |
| Released | May 2024 | July 2024 |
Two numbers do most of the heavy lifting here: parameters (35B vs 12B) and context (8K vs 128K). Everything downstream — hardware cost, latency, and use-case fit — flows from that pair.
Multilingual quality: where Aya was built to win
Aya 23's entire thesis is depth over breadth. The Aya 23 paper frames it explicitly as an experiment in allocating more capacity to 23 pre-training languages rather than the 101 covered by the original Aya. The payoff shows up on multilingual discriminative and generative benchmarks, where Aya-23-35B reports the highest scores across its language set among in-class open models at release.
The 23 languages include Arabic, Chinese (simplified and traditional), Czech, Dutch, English, French, German, Greek, Hebrew, Hindi, Indonesian, Italian, Japanese, Korean, Persian, Polish, Portuguese, Romanian, Russian, Spanish, Turkish, Ukrainian, and Vietnamese. Crucially, it is the lower-resource languages — Persian, Hebrew, Vietnamese, Ukrainian — where Aya opens the clearest gap over general-purpose models.
Mistral Nemo officially targets English, French, German, Spanish, Italian, Portuguese, Chinese, Japanese, Korean, Arabic, and Hindi. Its Hugging Face model card emphasizes strong performance across those mainstream languages and a new Tekken tokenizer that is ~30% more efficient on source code and many non-English scripts. Nemo is genuinely multilingual — but it is a generalist that happens to speak several languages well, not a model optimized for multilingual coverage as its primary goal.
| Benchmark (higher is better) | Aya 23 35B | Mistral Nemo 12B | Notes |
|---|---|---|---|
| Multilingual MMLU (avg) | ~48 | ~40–44 | Aya paper vs vendor figures; Aya leads on non-English subsets |
| Low-resource language win-rate | Strong | Moderate | Aya designed for exactly this |
| English MMLU | ~58 | ~68 | Nemo's generalist strength shows in English |
| Long-context retrieval | Limited (8K) | Excellent (128K) | Not a contest above 8K tokens |
Read the table as a shape, not a leaderboard: Aya is the sharper tool inside its multilingual lane, especially for languages that generalist models underserve. Nemo is stronger in English and vastly more capable once a task needs more than 8K tokens of context. Treat the numbers as directional — they come from different eval harnesses and vendor reporting, and our methodology page explains why we avoid presenting cross-source benchmarks as if they were run identically.
Hardware and VRAM: the 3x tax
This is where the match-up gets decided for most readers. A 35B model is not a casual download. At Q4_K_M quantization Aya 23 35B needs roughly 20–21 GB just for weights, plus KV cache and overhead — realistically a 24 GB GPU (RTX 3090 / 4090) or better. Mistral Nemo 12B at the same quant is around 7 GB and runs happily on a 12 GB card like an RTX 3060 12 GB or 4070.
| Config (Q4_K_M) | Aya 23 35B | Mistral Nemo 12B |
|---|---|---|
| Approx. weights on disk | ~21 GB | ~7.1 GB |
| Min practical VRAM | 24 GB | 10–12 GB |
| Comfortable GPU | RTX 4090 / A6000 | RTX 3060 12 GB / 4070 |
| CPU-only viable? | Painful (<3 tok/s typical) | Usable on modern CPUs |
| Full 128K context cost | N/A (8K cap) | Large KV cache — budget 16 GB+ GPU for long context |
The practical consequence: on a single mainstream 12 GB GPU, Aya 23 35B is effectively out of reach at usable quants, while Mistral Nemo 12B leaves headroom for a long context window. If your hardware ceiling is 12–16 GB of VRAM, the match-up is over before quality is even discussed. Estimate your electricity and amortized-hardware cost with our cost calculator before committing to the 35B tier — the ongoing power draw of a 24 GB card running continuously is not trivial.
Context window: 8K vs 128K changes the job
Aya 23's 8,192-token window is a real constraint in 2026. It is fine for chat turns, single-document translation, and short-form generation — exactly Aya's wheelhouse. But it rules the model out of long-document summarization, large-codebase reasoning, and retrieval-augmented generation over big chunks without aggressive chunking.
Mistral Nemo's 128K window (16x larger) is one of its headline features and, per the Mistral announcement, a deliberate design choice. For agentic workflows, multi-file code review, or feeding an entire contract for multilingual analysis, Nemo simply does jobs Aya cannot attempt. If your multilingual task also involves long inputs — legal, technical, or transcript-heavy content — Nemo's context advantage often outweighs Aya's per-language edge.
Licensing and commercial use
Do not skip this. Aya 23 is released under CC-BY-NC 4.0 plus Cohere's acceptable-use policy — non-commercial. That is perfect for research, evaluation, and personal projects, but it blocks direct use in a paid product or internal commercial service. Mistral Nemo ships under Apache 2.0, one of the most permissive licenses available, with no commercial restriction.
If you are building anything you intend to monetize, Aya 23's license alone can end the comparison. Verify current terms on each model card before deployment — licenses do change.
Running each model locally
Both are one command away via Ollama. The following gets you a working multilingual endpoint; swap the quant tag to match your VRAM.
# Mistral Nemo 12B — fits a 12 GB GPU
ollama run mistral-nemo:12b
# Aya 23 35B — needs 24 GB VRAM at Q4
ollama run aya:35bModel tags and available quantizations are listed on the Ollama Mistral Nemo page and the Aya library entry. For structured, reproducible comparisons we publish every spec used on this site through the BestLLMfor public API (CC BY 4.0) and an open-source MCP server, so you can pull the same numbers programmatically into your own tooling rather than copy-pasting from tables. Browse the full model list on our catalog or the head-to-head benchmarks hub.
Verdict: which multilingual model should you run?
There is no universal winner — there is a winner per constraint. If your product is multilingual quality, especially in lower-resource languages, and you have a 24 GB GPU plus a non-commercial or research context, Aya 23 35B is the better instrument and it is not close on its home turf. For nearly everyone else — commercial use, modest hardware, long documents, or agentic pipelines — Mistral Nemo 12B is the pragmatic pick and the one we default to recommending.
| Your priority | Winner | Why |
|---|---|---|
| Best low-resource multilingual quality | Aya 23 35B | Purpose-built across 23 languages |
| Runs on a 12 GB GPU | Mistral Nemo 12B | ~7 GB at Q4_K_M |
| Long context / RAG / agents | Mistral Nemo 12B | 128K vs 8K window |
| Commercial deployment | Mistral Nemo 12B | Apache 2.0 license |
| Research & translation depth | Aya 23 35B | Higher multilingual accuracy |
| Best all-round value | Mistral Nemo 12B | Quality-per-VRAM and licensing |
For more recommendations by use case, see our best models shortlists and the broader guides library.
Frequently Asked Questions
Is Aya 23 35B better than Mistral Nemo 12B for translation?
Generally yes, especially for the 23 languages Aya was trained on and particularly for lower-resource languages like Persian, Hebrew, Vietnamese, and Ukrainian. Aya 23 was explicitly optimized for multilingual depth, so it tends to produce more idiomatic output in those languages. Mistral Nemo is still a competent translator for mainstream languages and wins when long documents exceed Aya's 8K context.
Can I run Aya 23 35B on a 12 GB GPU?
Not comfortably. At Q4_K_M the weights alone are around 21 GB, so you realistically need a 24 GB GPU. On a 12 GB card you would have to offload heavily to system RAM, which drops throughput to a few tokens per second. Mistral Nemo 12B is the far better fit for 12 GB VRAM.
Is Mistral Nemo the same as Mistral Nemotron?
No. Mistral Nemo 12B is a July 2024 open-weight model under Apache 2.0. Mistral Nemotron is a separate, later model. Several comparison pages conflate the two — this guide covers only Mistral Nemo 12B.
Which model has the larger context window?
Mistral Nemo 12B, by a wide margin: 128K tokens versus Aya 23's 8K. For long-document, RAG, or agentic tasks, Nemo is the only viable choice of the two.
Can I use these models commercially?
Mistral Nemo 12B is Apache 2.0 and free for commercial use. Aya 23 35B is CC-BY-NC 4.0 (non-commercial) plus an acceptable-use policy, so it cannot be used directly in a paid product. Always re-check the current license on each model card before shipping.
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