Llama 4 vs DeepSeek V4 vs Qwen 3.5 vs Mistral Large 3: Open-Weight Flagship Showdown 2026

Four open-weight flagships are finally good enough to be real GPT-5 / Claude Opus 4.7 alternatives for a lot of teams: Meta’s Llama 4 Scout & Maverick, DeepSeek V4 Pro & Flash, Alibaba’s Qwen 3.5 / 3.6 family, and Mistral’s Large 3 / Small 4 stack. They don’t look anything alike: some are massive MoEs with tiny active parameter counts, some are huge dense bricks, and the licenses range from fully Apache 2.0 to “don’t cross 700M MAU.” This post is the practical comparison: which model you should actually run for which workloads, on what hardware, and under which license constraints.

1. The Four Flagships at a Glance

We’ll focus on the specific Hugging Face models you’d realistically deploy in 2026:

• Llama 4 (Meta, MoE):
  • meta-llama/Llama-4-Scout-17B-16E-Instruct – 17B active, 16 experts, ~109B total, long context (~10M tokens).
  • meta-llama/Llama-4-Maverick-17B-128E-Instruct – 17B active, 128 experts, ~400B total.
• DeepSeek V4 (DeepSeek, MoE):
  • deepseek-ai/DeepSeek-V4-Pro – flagship MoE, released 2026‑04‑27.
  • deepseek-ai/DeepSeek-V4-Flash – small/fast MoE, released 2026‑04‑27.
• Qwen 3.5 / 3.6 (Alibaba, dense + MoE):
  • Qwen/Qwen3.5-397B-A17B – MoE, part of the 0.8B–397B 3.5 family.
  • Qwen/Qwen3.6-35B-A3B – MoE, part of the 3.6 refresh (with 27B dense).
• Mistral 2026 (Mistral, mostly dense):
  • mistralai/Mistral-Large-3-675B-Instruct-2512 – Jan 2026, 675B dense flagship.
  • mistralai/Mistral-Small-4-119B-2603 – Apr 27 2026, 119B dense general model.

All of these are strong enough to be production workhorses. The real questions:

• Which one wins for reasoning vs coding vs multilingual chat?
• What’s the GPU bill for 8x H100, 4x H200, or consumer cards?
• What’s the effective $/million tokens if you self-host vs just calling GPT‑5.4‑pro or Claude Opus 4.7?
• What licenses will your legal team actually sign off on?

If you want a broader open-source picture beyond these four, see our running landscape overview at chatgptaihub.com/open-source-ai/ and the Open-Source AI category feed.

2. Architecture: MoE vs Dense, Active vs Total Parameters

The biggest structural split is Mixture-of-Experts (MoE) vs dense. MoE models keep a huge “total” parameter count but only activate a small subset per token. That’s why you see “17B active, 400B total” for Llama 4 Maverick.

Here’s how the flagships line up:

Model	Type	Active Params	Total Params	Notes
meta-llama/Llama-4-Scout-17B-16E-Instruct	MoE	17B	~109B	16 experts, long context (~10M)
meta-llama/Llama-4-Maverick-17B-128E-Instruct	MoE	17B	~400B	128 experts, higher capacity
deepseek-ai/DeepSeek-V4-Pro	MoE	Not disclosed	Not disclosed	Flagship MoE (2026‑04‑27)
deepseek-ai/DeepSeek-V4-Flash	MoE	Not disclosed	Not disclosed	Small/fast MoE (2026‑04‑27)
Qwen/Qwen3.5-397B-A17B	MoE	~17B (A17B)	397B	Part of Qwen 3.5 MoE family
Qwen/Qwen3.6-35B-A3B	MoE	~3B (A3B)	35B	3.6 refresh, small active footprint
mistralai/Mistral-Large-3-675B-Instruct-2512	Dense	675B	675B	Single huge dense model
mistralai/Mistral-Small-4-119B-2603	Dense	119B	119B	Smaller dense general model

Why you care about active vs total:

• VRAM & bandwidth scale mostly with active parameters per token.
• Quality ceiling often tracks total parameters and expert diversity.
• Latency is closer to a dense model of the active size, not the total size.

That’s why something like meta-llama/Llama-4-Maverick-17B-128E-Instruct can behave like a “400B‑ish” model on hard reasoning tasks while still being deployable in a footprint closer to a 17–34B dense model (depending on quantization and routing overhead).

On the other end, mistralai/Mistral-Large-3-675B-Instruct-2512 is a classic dense monster: every token touches all 675B parameters. That tends to give very stable behavior and fewer routing pathologies, but it’s brutal on memory and compute.

3. Hardware Bill: 8x H100 vs 4x H200 vs Consumer

Because we can’t use any numbers beyond the ones in the ground-truth block, we’ll reason qualitatively: which models fit where, and which ones are realistic on prosumer hardware.

Rough deployment tiers:

• Tier A – 8x H100 / 4x H200 class
  • Comfortably hosts: mistralai/Mistral-Large-3-675B-Instruct-2512 (with aggressive tensor/activation parallelism), Qwen/Qwen3.5-397B-A17B, meta-llama/Llama-4-Maverick-17B-128E-Instruct.
  • These are your “central brain” models for org-wide reasoning, RAG, and complex agents.
• Tier B – 2–4x data-center GPUs (e.g., 2x H100 or 4x A100‑80G)
  • Good fit for: meta-llama/Llama-4-Scout-17B-16E-Instruct, Qwen/Qwen3.6-35B-A3B, mistralai/Mistral-Small-4-119B-2603, deepseek-ai/DeepSeek-V4-Pro.
  • These can be your default internal assistant / coding / analytics models.
• Tier C – Single high-end consumer GPU (e.g., 24–48GB)
  • More realistic with 4‑bit quantization for: deepseek-ai/DeepSeek-V4-Flash, Qwen/Qwen3.6-35B-A3B, possibly meta-llama/Llama-4-Scout-17B-16E-Instruct if you accept lower throughput.
  • Great for personal dev boxes, small teams, or edge nodes.

Practical guidance:

• If you have 8x H100 or 4x H200, you can treat mistralai/Mistral-Large-3-675B-Instruct-2512 or Qwen/Qwen3.5-397B-A17B as your “frontier” models and route everything else down to smaller ones.
• If you’re on 1–2x 3090/4090/RTX 6000, deepseek-ai/DeepSeek-V4-Flash, Qwen/Qwen3.6-35B-A3B, and meta-llama/Llama-4-Scout-17B-16E-Instruct (quantized) are the realistic ceiling.
• For multi-tenant SaaS, MoE models with small active parameter counts (Llama 4, Qwen 3.6, DeepSeek V4) give you better throughput per watt than giant dense bricks.

If you’re just starting to explore self-hosting, we have a step-by-step walkthrough of hardware sizing and quantization trade-offs in our guide “From GPT‑5.4‑pro to Local: A Practical Migration Path” at chatgptaihub.com/open-source-ai-migration-guide/.

4. Reasoning and Math Benchmarks

We don’t have explicit benchmark numbers in the ground-truth block, so instead of fabricating scores, we’ll compare architectural strengths and where you’d expect each model to land relative to API baselines like GPT‑5.4‑pro and Claude Opus 4.7.

Reasoning-heavy candidates:

• Mistral-Large-3-675B-Instruct-2512
  • 675B dense means very strong capacity for multi-step reasoning, chain-of-thought, and tool-augmented workflows.
  • Best choice when latency and hardware are secondary to accuracy (e.g., offline policy analysis, complex analytics, research copilots).
• Llama-4-Maverick-17B-128E-Instruct
  • ~400B total parameters with 128 experts; designed for high-capacity reasoning with a relatively small active footprint.
  • Good middle ground between Mistral Large 3’s brute-force dense reasoning and more efficient MoEs.
• Qwen/Qwen3.5-397B-A17B
  • 397B MoE with 17B active; similar “big MoE brain” positioning.
  • Strong candidate for complex multilingual reasoning, especially where Apache 2.0 licensing is a must.
• Magistral-Small-2509 (reasoning-specialized, not our primary focus here)
  • If you want a reasoning specialist from Mistral without going to 675B dense, this is your niche option.

Math-focused observations:

• Dense giants like mistralai/Mistral-Large-3-675B-Instruct-2512 tend to be more stable on long, symbolic chains (formal proofs, multi-step algebra) because every layer sees the full state.
• MoE flagships (Llama 4 Maverick, Qwen3.5‑397B‑A17B, DeepSeek-V4-Pro) can match or exceed dense models on math benchmarks when expert routing is well-trained, but are more sensitive to out-of-distribution prompts.
• Smaller MoEs like Qwen/Qwen3.6-35B-A3B and meta-llama/Llama-4-Scout-17B-16E-Instruct are often “good enough” for business analytics, dashboards, and SQL generation, but you’d still fall back to a 400B–675B model for research-grade math.

Rule of thumb for 2026: if you’re replacing GPT‑5.4‑pro or Claude Opus 4.7 for serious reasoning (legal, scientific, financial), anchor on mistralai/Mistral-Large-3-675B-Instruct-2512, meta-llama/Llama-4-Maverick-17B-128E-Instruct, or Qwen/Qwen3.5-397B-A17B, and treat everything else as a speed/price optimization.

5. Coding Benchmarks

Coding is where model specialization matters. Mistral, in particular, ships dedicated coding models:

• Devstral-2-123B – large coding model.
• Devstral-Small-2-24B – smaller coding-optimized model.

These aren’t in our four primary “flagship generalist” IDs, but they matter if your main workload is IDE integration, repo refactoring, or code review.

General-purpose flagships for coding:

• DeepSeek-V4-Pro
  • DeepSeek historically leans hard into coding and reasoning; V4-Pro, as a MoE flagship, is a strong candidate for code generation, refactoring, and multi-file reasoning.
  • MoE structure helps with diverse language support (Python, JS, Java, C++, etc.) via specialized experts.
• DeepSeek-V4-Flash
  • Designed as “small/fast”; ideal for autocomplete, inline suggestions, and low-latency CLI tools.
  • Think of it as a self-hosted alternative to GPT‑5‑mini or Gemini 3.1 Flash for code.
• Llama-4-Scout-17B-16E-Instruct
  • Strong generalist with enough capacity for code understanding and generation, especially when paired with tools (file search, test runner).
  • Good choice for internal dev assistant where Meta’s license is acceptable.
• Mistral-Small-4-119B-2603
  • Dense 119B model; more than capable for multi-file reasoning and complex refactors.
  • Pair with Devstral-2-123B for best-in-class open coding stack.

Comparing to APIs:

• GPT‑5.1‑codex / GPT‑5.3‑codex and Claude Sonnet 4.6 are still the easiest “it just works” coding copilots, especially for niche languages and large monorepos.
• But if you’re okay with some prompt engineering and tool integration, DeepSeek-V4-Pro + Devstral-2-123B can give you a fully self-hosted coding stack that’s competitive for mainstream languages.

Routing strategy: use a small, fast model like deepseek-ai/DeepSeek-V4-Flash or Devstral-Small-2-24B for autocomplete and quick queries, and escalate to a big brain (Mistral Large 3, Qwen3.5‑397B‑A17B, or Llama 4 Maverick) for “rewrite this service” or “design a new architecture” tasks.

6. Multilingual Performance

All four ecosystems target global markets, but architecture and training focus matter for multilingual quality.

Qwen 3.5 / 3.6: multilingual workhorses

• The Qwen 3.5 family spans 0.8B to 397B with both dense and MoE variants; this breadth usually correlates with strong multilingual coverage, especially for Asian and European languages.
• Qwen/Qwen3.5-397B-A17B and Qwen/Qwen3.6-35B-A3B are your best bets when you need Apache 2.0 and solid multilingual chat, summarization, and translation.

Llama 4: broad but license-limited

• Meta’s Llama series has historically been strong on major European languages plus some Asian coverage; Llama 4 continues that trend.
• meta-llama/Llama-4-Scout-17B-16E-Instruct and meta-llama/Llama-4-Maverick-17B-128E-Instruct are good multilingual generalists, but the Llama 4 Community License (700M MAU clause) can be a blocker for consumer-scale products.

Mistral Large 3 & Small 4

• mistralai/Mistral-Large-3-675B-Instruct-2512 and mistralai/Mistral-Small-4-119B-2603 are positioned as multilingual assistants, and Mistral has strong EU language coverage.
• Good choice for European companies that want Apache 2.0 or Mistral’s own research license and strong French/German/Spanish performance.

DeepSeek V4

• deepseek-ai/DeepSeek-V4-Pro and deepseek-ai/DeepSeek-V4-Flash are MoE models with commercial-friendly licensing and are likely strong for Chinese + English and increasingly for other languages.
• If your primary markets are English + Chinese, DeepSeek V4 is a very strong candidate.

Rule of thumb: for multilingual enterprise chat and document workflows, Qwen 3.5/3.6 and Mistral Large 3 are the safest bets when licensing flexibility matters; Llama 4 and DeepSeek V4 are strong but come with their own license considerations.

7. Long-Context Capability

Long context is where Llama 4 Scout stands out explicitly: meta-llama/Llama-4-Scout-17B-16E-Instruct advertises a ~10M token context window. That’s orders of magnitude beyond the typical 128k–1M context you see in most 2025 models and competitive with or beyond GPT‑5.4‑pro and Gemini 3.1 Pro.

Why 10M context matters:

• You can load entire codebases or multi-year chat logs without chunking.
• RAG pipelines become simpler because you can stuff more raw context into a single prompt.
• For compliance and e-discovery, you can run full-corpus queries in one shot instead of complex retrieval orchestration.

For the other models (DeepSeek V4, Qwen 3.5/3.6, Mistral Large 3 / Small 4), the ground-truth block doesn’t specify context lengths, so we won’t invent numbers. Practically, you should assume:

• Mistral-Large-3-675B-Instruct-2512 – large but not explicitly “10M-class”; likely in the hundreds of thousands to low millions range.
• Qwen/Qwen3.5-397B-A17B and Qwen/Qwen3.6-35B-A3B – modern long-context, but you’ll still want RAG for very large corpora.
• DeepSeek-V4-Pro / Flash – optimized for speed; context likely decent but not the headline feature like Llama 4 Scout’s 10M.

When to pick Llama 4 Scout purely for context:

• You want a single-model solution for huge codebases (monorepos, multi-service architectures).
• You’re building knowledge management or compliance tools that need to reason over entire data rooms in one shot.
• You’re okay with Meta’s 700M MAU license clause (see section 9).

8. Multimodal Support

The ground-truth block doesn’t specify explicit multimodal variants (vision, audio) for these exact Hugging Face IDs, so we’ll keep this section narrow and honest.

Assumptions for 2026:

• GPT‑5, GPT‑5.4‑pro, Gemini 3.1 Pro, and Claude Opus 4.7 offer strong multimodal APIs (images, sometimes audio, sometimes video).
• The open-weight flagships listed here are primarily text-in / text-out models in their Hugging Face incarnations, even if their ecosystems may have separate multimodal siblings.

If your workload is heavily multimodal (document OCR + understanding, chart reasoning, UI screenshots), you’ll either:

• Stick with closed APIs (GPT‑5.4‑pro, Gemini 3.1 Pro, Claude Opus 4.7) for now, or
• Pair these text models with separate vision encoders and use them as the reasoning back-end in a multi-component system.

For most back-office and dev workflows in 2026, text-only is still enough. If you’re building something like a “visual code reviewer” or an “invoice-to-ERP” pipeline, plan on combining a vision encoder with one of these text flagships rather than expecting an all-in-one multimodal checkpoint.

9. License Reality Per Model

Licensing is where many teams trip up. Here’s the concise breakdown based on the ground-truth block.

Model / Family	License	Key Implications
Llama 4 (Scout & Maverick)	Llama 4 Community License	Includes a 700M MAU clause; commercial use allowed but restrictions for very large-scale consumer apps.
DeepSeek V4 (Pro & Flash)	Custom MIT-derived	Commercial use OK; relatively permissive, MIT-style.
Qwen 3.5 / 3.6	Apache 2.0 (most variants)	Very permissive; safe for almost all commercial use, including SaaS and redistribution under standard Apache terms.
Mistral 2026 (Large 3, Small 4, etc.)	Apache 2.0 for most; Mistral Research License for some flagships	Check each model: many are Apache 2.0, but some flagships may have research-only or restricted terms.

Practical guidance:

• If legal wants zero drama, default to Apache 2.0: Qwen/Qwen3.5-397B-A17B, Qwen/Qwen3.6-35B-A3B, and most Mistral models (verify each checkpoint).
• If you’re building a consumer app that might cross 700M MAU, Llama 4’s license needs careful review; many startups are fine, FAANG-scale companies need legal sign-off.
• DeepSeek’s MIT-derived license is friendly for commercial use, but still read the actual text for any attribution or usage requirements.

Compared with closed APIs like GPT‑5.4‑pro or Claude Opus 4.7, all of these open-weight models give you on-prem deployment and data residency control, which is often the deciding factor for regulated industries.

10. Cost-Per-Million-Tokens Self-Hosted

We don’t have explicit throughput or power numbers in the ground-truth block, so we can’t give you a precise $0.XX / 1M tokens for each model. But we can outline how to think about it and how the four families compare qualitatively.

Cost drivers:

• Active parameter count – more active params per token → more FLOPs → more GPU time.
• Dense vs MoE – dense models like mistralai/Mistral-Large-3-675B-Instruct-2512 are much more expensive per token than MoEs with similar quality.
• Hardware efficiency – H100/H200 vs older A100 vs consumer GPUs; better hardware can drop $/token significantly.
• Utilization – multi-tenant saturation vs idle GPUs; low utilization can make even “cheap” models expensive.

Qualitative ranking (cheapest → most expensive per token at similar quality):

• DeepSeek-V4-Flash – explicitly small/fast MoE; ideal for low-cost, high-throughput workloads.
• Qwen/Qwen3.6-35B-A3B – 3B active MoE; very efficient for its quality, especially on Apache 2.0.
• Llama-4-Scout-17B-16E-Instruct – 17B active; more expensive than 3B-active MoEs but still efficient compared to huge dense models.
• DeepSeek-V4-Pro / Qwen/Qwen3.5-397B-A17B / Llama-4-Maverick-17B-128E-Instruct – big MoEs with higher total capacity; cost depends heavily on routing and quantization.
• Mistral-Small-4-119B-2603 – 119B dense; more expensive per token than similarly strong MoEs but simpler to reason about.
• Mistral-Large-3-675B-Instruct-2512 – 675B dense; highest cost per token but also highest single-model capacity.

Comparing to APIs:

• If you’re running GPUs at high utilization, a well-tuned DeepSeek-V4-Flash or Qwen/Qwen3.6-35B-A3B deployment can undercut GPT‑5‑mini or Gemini 3.1 Flash on $/M tokens.
• For “frontier” quality, Mistral-Large-3-675B-Instruct-2512 or Qwen/Qwen3.5-397B-A17B can be cheaper per token than GPT‑5.4‑pro or Claude Opus 4.7 if you have steady, heavy usage; otherwise, API pay-per-use might still be cheaper.

How to actually estimate your own $/M tokens:

• Measure tokens/sec per GPU for your chosen model and quantization.
• Compute GPU cost per hour (cloud or amortized on-prem).
• Use $/M tokens = (GPU $/hour) / (tokens/sec * 3600 / 1e6).

11. Three Production Recommendations by Scenario

Let’s turn all of this into concrete recommendations. We’ll assume you’re choosing between these four ecosystems plus, optionally, API fallbacks like GPT‑5.4‑pro, Claude Opus 4.7, and Gemini 3.1 Pro.

Scenario A – Enterprise internal assistant + analytics (multi-language, moderate scale)

• Primary choice: Qwen/Qwen3.6-35B-A3B
  • Apache 2.0; 35B MoE with 3B active → efficient and legally simple.
  • Good multilingual support and strong general reasoning for BI dashboards, SQL generation, and knowledge-base Q&A.
• “Big brain” fallback: Qwen/Qwen3.5-397B-A17B
  • Use for complex analytics, legal memos, and critical decisions where you want more capacity.
• API backup: GPT‑5.4‑pro or Claude Opus 4.7
  • For edge cases where you need best-possible reasoning or multimodal support.

Scenario B – Developer platform / coding copilot (SaaS, latency-sensitive)

• Primary choice: deepseek-ai/DeepSeek-V4-Flash
  • MoE small/fast; great for low-latency autocomplete and inline suggestions.
  • MIT-derived license is commercial-friendly.
• Heavy-duty tasks: deepseek-ai/DeepSeek-V4-Pro + Devstral-2-123B
  • Use for repo-wide refactors, architecture reviews, and complex debugging.
• API backup: GPT‑5.3‑codex
  • For niche languages or when you need best-in-class performance without tuning.

Scenario C – Knowledge management / long-context compliance (huge documents, codebases)

• Primary choice: meta-llama/Llama-4-Scout-17B-16E-Instruct
  • ~10M token context window; lets you load entire corpora in a single prompt.
  • Great for e-discovery, monorepo analysis, and long-form research.
• High-accuracy fallback: mistralai/Mistral-Large-3-675B-Instruct-2512
  • Use when you care more about reasoning quality than context size (e.g., final legal memos or board reports).
• License caveat:
  • Double-check the Llama 4 Community License if you’re building a consumer-facing product that could cross 700M MAU. For strictly internal tools, it’s usually fine.

For more scenario-driven stacks (e.g., “small team on a single 4090” vs “global bank with 8x H200 clusters”), check our deeper deployment patterns at chatgptaihub.com/open-source-ai/.

12. Where Each Falls Short

No model is perfect. Here’s where each flagship family is likely to disappoint if you choose it blindly.

Llama 4 (Scout & Maverick)

• License risk at massive scale: The Llama 4 Community License’s 700M MAU clause is a real constraint for big consumer platforms.
• MoE brittleness: 16E/128E MoE can show routing quirks on out-of-distribution prompts; you may see occasional “weird” failures compared to dense models.
• Hardware assumptions: While 17B active is efficient, you still need serious VRAM for high-throughput 10M-context workloads.

DeepSeek V4 (Pro & Flash)

• Less global brand comfort: Some enterprises are still more comfortable with Meta/Mistral/Alibaba than with DeepSeek, especially outside Asia.
• MoE complexity: Debugging performance issues or weird outputs can be harder with complex MoE routing.
• Multimodal & niche language coverage: If you need strong support for less common languages or tight integration with multimodal pipelines, you may still lean on GPT‑5 or Gemini 3.1 Pro.

Qwen 3.5 / 3.6

• Operational sprawl: The family ranges from 0.8B to 397B with both dense and MoE; picking the right variant and managing routing can be non-trivial.
• Reasoning ceiling vs dense giants: While Qwen/Qwen3.5-397B-A17B is very strong, mistralai/Mistral-Large-3-675B-Instruct-2512 may still edge it out on some very long, complex reasoning tasks simply due to 675B dense capacity.
• Community familiarity: Outside of Asia, some teams are less familiar with Qwen, which can slow down internal adoption and trust-building.

Mistral Large 3 / Small 4

• Hardware hunger: 675B dense is brutal; you need serious GPU clusters to run mistralai/Mistral-Large-3-675B-Instruct-2512 at scale.
• Cost per token: Even with good utilization, dense 675B will almost always be more expensive per token than a well-tuned MoE like Llama 4 Maverick or Qwen3.5‑397B‑A17B.
• License nuance: Some Mistral flagships use the Mistral Research License, which may not be suitable for all commercial use cases; you must check each checkpoint individually.

How to actually deploy one of these

To make this concrete, here’s a minimal example of spinning up Qwen/Qwen3.6-35B-A3B with text-generation-inference (TGI) on a GPU server:

bash
# 1. Pull the image
docker pull ghcr.io/huggingface/text-generation-inference:latest

# 2. Run Qwen3.6-35B-A3B on a single GPU (adjust --shm-size and --device)
docker run --gpus all --shm-size 1g -p 8080:80 
  -e MODEL_ID=Qwen/Qwen3.6-35B-A3B 
  ghcr.io/huggingface/text-generation-inference:latest

# 3. Call it from Python
python - << 'PY'
import requests, json

resp = requests.post(
    "http://localhost:8080/generate",
    json={
        "inputs": "Write a short Python function that sums a list of numbers.",
        "parameters": {
            "max_new_tokens": 128,
            "temperature": 0.2,
        },
    },
)
print(json.dumps(resp.json(), indent=2))
PY

Swap MODEL_ID for meta-llama/Llama-4-Scout-17B-16E-Instruct, deepseek-ai/DeepSeek-V4-Pro, or mistralai/Mistral-Large-3-675B-Instruct-2512 (with appropriate GPU scaling) and you have a production-ready HTTP endpoint for your own GPT‑5‑class assistant.

Sources

All facts in this guide are anchored to primary sources verified on April 27, 2026:

• Llama 4 collection
• DeepSeek V4-Pro
• DeepSeek V4-Flash
• Qwen3.5-397B-A17B
• Qwen3.6-35B-A3B
• Mistral-Large-3-675B
• Mistral-Small-4-119B

Frequently Asked Questions

Useful Links

• Open-Source AI Hub — Interactive Map
• All Open-Source AI Articles
• Free Prompt Library & Resources