Which is the best open multimodal model in April 2026?

For unified text + image + audio + video reasoning in a single model: NVIDIA Nemotron 3 Nano Omni (released April 28, 2026) is the strongest open choice. For pure text + vision and ecosystem maturity: Meta Llama 5. For Asia-language coverage and aggressive licensing: Alibaba Qwen 3.6. Nemotron is the new default for multimodal agents; Llama 5 is the default for general-purpose; Qwen for non-English and Apache-licensed self-hosting.

Does Nemotron 3 Nano Omni beat Llama 5 on text?

No, not yet. On pure text reasoning benchmarks (MMLU-Pro, GPQA, AIME), Llama 5's larger flagship variants still lead among open models in April 2026. Nemotron 3 Nano Omni is competitive but optimized for unified multimodal agents, not for being the best at single-modality text. If your workload is text-only, Llama 5 or Qwen 3.6 is a better pick.

What hardware do you need to run these models?

Nemotron 3 Nano Omni (30B hybrid MoE, ~3B active per token, 256K context): single H100 at FP8 or H200 at full precision; consumer 5090 with 4-bit quant. Llama 5 ranges from edge (8B) to flagship (~400B+) — flagship needs an H200/B200 cluster. Qwen 3.6 open weights are a 35B-A3B MoE and a dense 27B (both fit on one 80GB GPU at FP8); its successor Qwen 3.8 adds a 27B and the 2.4T-parameter Qwen3.8-Max. For most teams, the 30-70B range is the sweet spot — all three families have models in that range.

Which has the best license for commercial use?

Qwen 3.6 — Apache 2.0 across most variants, no usage restrictions. Nemotron 3 Nano Omni — NVIDIA Open Model License, generally permissive but read the terms. Llama 5 — Meta's custom Llama 5 community license with a 700M monthly active users threshold and other restrictions. For maximum commercial flexibility: Qwen 3.6. For NVIDIA stack alignment: Nemotron. For ecosystem and tooling: Llama 5.

Nemotron vs Llama: what are the use cases for each AI model?

Use NVIDIA Nemotron when one open model has to understand video, audio, images and text together (meeting and call recordings, screen-recording support agents, document and chart OCR, GUI agents) or when you deploy on NVIDIA hardware with NIM and TensorRT-LLM; Nemotron 3 Nano Omni (30B, ~3B active, 256K context) is the multimodal pick and Nemotron 3 Super (120B-A12B) and Ultra (550B-A55B) cover text reasoning and agents. Use Meta's Llama for general-purpose text and vision apps where ecosystem support (vLLM, llama.cpp, Ollama, fine-tunes) matters most. As of October 2026, Meta's newest open weights on Hugging Face are still the Llama 4 family; Meta's 2026 frontier models are the proprietary Muse line.

Quick Answer

Nemotron 3 Nano Omni vs Llama 5 vs Qwen 3.6 (October 2026)

Published: April 30, 2026 • Updated: October 9, 2026

Nemotron 3 Nano Omni vs Llama 5 vs Qwen 3.6 (October 2026)

Three open-weight model families now compete at the frontier. NVIDIA Nemotron 3 Nano Omni dropped April 28, 2026. Meta Llama 5 has been out since April 5. Qwen 3.6 from Alibaba updated through April. Here’s how they compare for production agent work.

Last verified: October 9, 2026. (First published April 30, 2026. Benchmark numbers below are from April 2026 vendor reports and were last confirmed then; model availability, sizes and licences were re-checked today against Hugging Face model cards and NVIDIA’s release pages.)

What changed as of October 2026

Nemotron 3 is complete. Nemotron 3 Super (120B total, 12B active) was already out on March 10–12, 2026 — before this page said it was “coming” — and Nemotron 3 Ultra (550B total, 55B active) followed on June 3–4, 2026 as the final and largest Nemotron 3 model. NVIDIA has since released Nemotron 3.5 Lightning (30B-A3B, August 2026). Nemotron 3 Nano Omni itself is unchanged: 30B total, ~3B active (not 8–12B as first written), 256K context, video/audio/image/text in, text out, NVIDIA Open Model Agreement licence.
Qwen moved to 3.8. Alibaba released Qwen3.8-27B (Apache-2.0, vision built in) on August 5, 2026, the 2.4-trillion-parameter Qwen3.8-Max (custom licence) on August 8 and Qwen3.8-Flash-Next on August 24. See Qwen3.8-Max explained.
Qwen 3.6 sizes, corrected. Qwen’s Hugging Face organization publishes Qwen 3.6 open weights as Qwen3.6-35B-A3B (April 15) and Qwen3.6-27B (April 21), both Apache-2.0 and both natively image+text. The 7B, 72B and 235B-MoE Qwen 3.6 sizes this page listed are not among those open-weight releases, and Qwen 3.6 does not need a separate Qwen-VL model for images.
Llama 5 could not be confirmed against a primary source today. Meta’s official Hugging Face organization (meta-llama) lists no Llama 5 weights — its newest models are the Llama 4 family from April 2025 — and third-party reports conflict on whether and when Llama 5 shipped. Meta’s 2026 frontier models are the proprietary Muse line (Muse Spark, April 2026). The Llama 5 details below are as reported on April 30, 2026 and were last confirmed then; treat them as unverified. See Claude Opus 5 vs Muse Spark 1.2.

TL;DR

Use case	Pick
Unified multimodal agents (text + image + audio + video)	Nemotron 3 Nano Omni
Pure text + vision, broadest ecosystem	Llama 5
Multilingual, Apache 2.0, cost-sensitive self-host	Qwen 3.6
On-prem with NVIDIA stack	Nemotron 3 Nano Omni
Edge / mobile inference	Llama 5 (8B variant) or Qwen 3.6 small

At a glance

	Nemotron 3 Nano Omni	Llama 5	Qwen 3.6
Vendor	NVIDIA	Meta	Alibaba
Released	Apr 28, 2026	Apr 5, 2026	Rolling, Apr 2026 update
Architecture	Hybrid MoE	Dense + MoE variants	MoE flagship
Largest size	30B (Nano Omni, ~3B active); family: Super 120B, Ultra 550B	400B+ flagship (as reported April 2026)	35B-A3B MoE (open weights)
Modalities	Text, image, audio, video	Text, image (vision via Llama 5V)	Text + image natively (open 3.6 models)
Audio in core model	✅ Native	❌ Separate model	❌ Separate model
License	NVIDIA Open Model License	Llama 5 Community License	Apache 2.0 (most variants)
Open training data	✅ 15 datasets released	Partial	Partial
Best at	Agentic multimodal	General-purpose, ecosystem	Multilingual, cost

Where each one wins

Nemotron 3 Nano Omni — multimodal agents

The key differentiator: audio is a first-class modality in the same model that sees images and reads text.

For agent workloads that mix media — customer service that watches a screen recording while listening to the call, monitoring agents that correlate dashboards with voice alerts, research agents that read papers and watch lecture videos — running one model is materially better than orchestrating Whisper + a vision model + an LLM.

NVIDIA also released the most open package: weights, 15 training datasets, RL trajectories, and tool-call data. For researchers fine-tuning on multimodal tasks, this is the most workable starting point in 2026.

Limits:

Pure text reasoning trails Llama 5 flagship and closed models.
Smaller context window than Gemini 3.1 Pro or Claude Opus 4.7.
Ecosystem is younger — fewer fine-tunes and integrations than Llama.

Llama 5 — the ecosystem default

The key differentiator: every tool already supports it.

Llama 5 is the safe choice. Released April 5, 2026, it has:

Variants from 8B (edge) to 400B+ (frontier).
Llama 5V multimodal vision sibling.
Day-one support in vLLM, TensorRT-LLM, llama.cpp, MLX, Ollama, LM Studio.
The largest fine-tune ecosystem (instruction-tuned, code-tuned, domain-tuned variants).
Strong instruction following and tool use.

For most production deployments where multimodal isn’t the core requirement, Llama 5 will be easier to ship.

Limits:

License has a 700M monthly active users threshold and other restrictions — fine for most companies, blocks the largest competitors.
Native audio not in the core model — need a separate ASR.
Flagship variant requires serious GPU infrastructure.

Qwen 3.6 — multilingual and Apache 2.0

The key differentiator: Apache 2.0 license and best non-English performance.

Alibaba ships aggressive licensing — Apache 2.0 means use it however you want, no thresholds. Qwen 3.6 is the strongest open model on Chinese, Japanese, Korean, and many Asian languages. It also competes well on English tasks at the 70-235B sizes.

For teams in or selling into Asia, or any team that needs the most permissive license, Qwen 3.6 is hard to beat.

Limits:

No native audio in the open Qwen 3.6 models (image and text are built in; this page originally said vision needed separate Qwen-VL models, which is not true of Qwen 3.6).
Smaller US/EU community than Llama.
Some training-data transparency questions remain.

Hardware reality

Model	Minimum (FP8/4-bit)	Production (full precision)
Nemotron 3 Nano Omni 30B	1× RTX 5090 (4-bit)	1× H100 (FP8) or H200
Llama 5 8B	1× RTX 4090	1× A100
Llama 5 70B	2× H100 (FP8)	4× H100
Llama 5 400B+	Cluster only	8× H200 / B200 cluster
Qwen 3.6 27B (dense)	1× RTX 5090 (4-bit)	1× H100
Qwen 3.6 35B-A3B MoE	1× RTX 5090 (4-bit)	1× H100

For most teams, the 27-70B range is the sweet spot — fits on 1-4 GPUs, runs fast, and quality is high. Nemotron 3 Nano Omni and Qwen 3.6 27B / 35B-A3B fit on a single GPU.

Benchmarks (representative, April 2026)

Reasoning and knowledge (MMLU-Pro, higher is better):

Llama 5 flagship: ~78
Qwen 3.6 (largest variant, as reported April 2026): ~76
Nemotron 3 Nano Omni: ~71

Multimodal understanding (MMMU, higher is better):

Nemotron 3 Nano Omni: ~73
Llama 5V: ~70
Qwen-VL 3.6: ~68

Audio + multimodal agentic tasks (NVIDIA-published):

Nemotron 3 Nano Omni: best in class (no direct open competitor with audio in-core)

Coding (HumanEval+):

Llama 5 Code variant: ~84
Qwen 3.6 Coder: ~83
Nemotron 3 Nano Omni: ~76

(Numbers from vendor reports and early third-party evals; expect movement as more independent benchmarks land.)

Which to pick

Build agents that mix audio, video, and text: Nemotron 3 Nano Omni. No other open model handles all four modalities natively in April 2026.

Build a general-purpose AI app and want maximum tooling: Llama 5. The ecosystem is years ahead.

Need permissive licensing or non-English performance: Qwen 3.6. Apache 2.0 plus strong multilingual is unmatched.

Sovereign or air-gapped deployment with NVIDIA hardware: Nemotron 3 Nano Omni. NVIDIA’s open data commitment, NIM microservices, and TensorRT-LLM tuning make it the smoothest path.

You don’t know yet: Start with Llama 5. Switch to Nemotron when audio/video matters or to Qwen when license matters.

What changes next

NVIDIA has flagged Nemotron 3 Super and Nemotron 3 Ultra for the first half of 2026 — expect these to push open multimodal reasoning further. Llama 5 will likely see point releases through the year. Qwen 3.7 is rumored. For now, this three-way race is the open-multimodal landscape, and Nemotron 3 Nano Omni’s April 28 release just shifted it.

Update, October 2026: Nemotron 3 Super (March 2026) and Ultra (June 2026) are both out, and both are text models — Nano Omni is still NVIDIA’s open all-modality model. Qwen skipped a public 3.7 and shipped Qwen 3.8 in August 2026. No Llama 5 weights appear on Meta’s official Hugging Face organization as of today.

Sources

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