Inkling vs Llama 4 vs Qwen 3.5: Open-Weight AI Models Compared in July 2026
Inkling vs Llama 4 vs Qwen 3.5: Open-Weight AI Models Compared in July 2026
Mira Murati's Thinking Machines Lab just dropped its first model — Inkling, a 975B parameter Mixture-of-Experts model with 41B active parameters, Apache 2.0 licensed, multimodal, and claiming a 1M context window. The open-weight AI landscape just got a serious new contender. But how does it actually stack up against the current kings: Meta's Llama 4 and Alibaba's Qwen 3.5?
We compare all three across benchmarks, architecture, hardware requirements, pricing, licensing, and real-world use cases. This Inkling vs Llama 4 vs Qwen 3.5 comparison is the first of its kind. This is the first comprehensive three-way comparison of these models — no other outlet has published this yet.
Inkling vs Llama 4 vs Qwen 3.5: Model Overviews
Before diving into benchmarks, let's establish what each model actually is. The Inkling vs Llama 4 vs Qwen 3.5 matchup represents three distinct architectural philosophies. The three represent different philosophies in the open-weight space.
Thinking Machines Inkling (975B/41B MoE)
Inkling is a Mixture-of-Experts model with 975B total parameters and 41B active parameters per forward pass. Released under Apache 2.0 — the most permissive open-source license — it supports text, image, and audio inputs natively. The 1M token context window is unprecedented for an open-weight model. Thinking Machines Lab, founded by former OpenAI CTO Mira Murati, positions Inkling as a general-purpose foundation model that refuses the "one-size-fits-all" approach of dense models.
Meta Llama 4 (Scout and Maverick)
Llama 4 comes in two variants: Scout (109B total, 17B active MoE) and Maverick (400B total, 128B active MoE). Both are Apache 2.0 licensed with a custom acceptable use policy. Scout targets single-GPU inference with its 10M context window; Maverick competes at the frontier tier. Meta's distribution through Hugging Face and major cloud providers gives it unmatched accessibility.
Alibaba Qwen 3.5 (Up to 10M Context)
Qwen 3.5 pushes the context boundary to 10 million tokens in its largest variant (72B dense, plus MoE versions). The Qwen series has consistently topped open LLM leaderboards. Alibaba's licensing is custom (Qwen License) — more permissive than Llama's but with attribution requirements. Qwen 3.5 excels at long-context reasoning and coding tasks.
Inkling vs Llama 4 vs Qwen 3.5: Benchmark Comparison
Benchmarks don't tell the whole story, but they're the only standardized way to compare model capabilities at launch. Here's how the three stack up on key evaluations:
| Benchmark | Inkling (975B/41B) | Llama 4 Maverick (400B/128B) | Qwen 3.5 72B |
|---|---|---|---|
| MMLU-Pro | 88.7% | 89.2% | 89.5% |
| GPQA Diamond | 87.2% | 86.8% | 88.1% |
| AIME 2026 | 97.1% | 96.4% | 95.8% |
| HumanEval | 89.6% | 91.2% | 92.4% |
| HLE (Hard) | 46.0% | 44.8% | 47.2% |
| Global-MMLU-Lite | 88.7% | 87.9% | 89.3% |
Key takeaway: All three models are tightly clustered at the frontier. Qwen 3.5 edges out on coding (HumanEval) and general knowledge (MMLU). Inkling leads on mathematical reasoning (AIME) and holds its own on GPQA. Llama 4 Maverick is competitive across the board but doesn't clearly dominate any single category.
The Inkling vs Llama 4 vs Qwen 3.5 benchmark comparison reveals something important: the performance gap between the top open-weight models has narrowed to 1-3 percentage points on most benchmarks. Your choice should depend on factors beyond raw scores — hardware, licensing, modality, and ecosystem.
Sources: TechCrunch: Thinking Machines Launches Inkling | Hugging Face: Thinking Machines Lab Models | Wired: Thinking Machines Lab Drops First Model
Hardware Requirements and Inference Costs
This is where the rubber meets the road for developers and companies self-hosting these models. Inkling vs Llama 4 vs Qwen 3.5 hardware costs will drive most deployment decisions.
VRAM Requirements (FP8/INT4 Quantized)
| Model | FP8 (1 GPU) | INT4 (Consumer GPU) | Recommended Setup |
|---|---|---|---|
| Inkling (41B active) | ~85 GB | ~45 GB | 2× H100 or 4× A100 80GB |
| Llama 4 Maverick (128B active) | ~260 GB | ~130 GB | 4-8× H100 80GB |
| Llama 4 Scout (17B active) | ~35 GB | ~18 GB | 1× H100 80GB or 2× A100 |
| Qwen 3.5 72B (dense) | ~145 GB | ~72 GB | 2× H100 80GB |
| Qwen 3.5 MoE variants | ~60-80 GB | ~30-40 GB | 1-2× H100 |
Inkling's MoE advantage is clear: at 41B active parameters, it's the most hardware-efficient frontier model. Scout is lighter but significantly less capable. For teams with limited GPU budgets, Inkling offers the best capability-per-GPU ratio among the three flagship models.
API Pricing (Together AI / Groq / Fireworks)
| Provider | Inkling | Llama 4 Maverick | Qwen 3.5 72B |
|---|---|---|---|
| Together AI | $1.20/M input, $1.80/M output | $2.50/M input, $3.00/M output | $0.90/M input, $1.20/M output |
| Groq | Not yet available | $0.70/M input, $0.90/M output | $0.60/M input, $0.80/M output |
| Fireworks | $1.00/M input, $1.50/M output | $2.00/M input, $2.50/M output | $0.80/M input, $1.00/M output |
Qwen 3.5 is the cheapest to run via API. Inkling sits in the middle. Llama 4 Maverick commands a premium — but Groq's ultra-fast inference makes it compelling for latency-sensitive applications.
Licensing, Customization, and Ecosystem
The license determines what you can actually do with the model commercially. In the Inkling vs Llama 4 vs Qwen 3.5 licensing showdown, Inkling's Apache 2.0 is the cleanest.
License Comparison
| Model | License | Commercial Use | Modification | Attribution |
|---|---|---|---|---|
| Inkling | Apache 2.0 | ✅ Unrestricted | ✅ Full | Required |
| Llama 4 | Apache 2.0 + AUP | ✅ With AUP compliance | ✅ Full | Required |
| Qwen 3.5 | Qwen License | ✅ With conditions | ✅ Full | Required |
Inkling's Apache 2.0 license is the cleanest — no acceptable use policy restrictions, no behavioral clauses. Llama 4's AUP prohibits certain use cases (military, surveillance, etc.). Qwen's license requires attribution and has some commercial restrictions for competitors.
Fine-tuning and Customization
All three support LoRA/QLoRA fine-tuning. Thinking Machines provides Tinker — a dedicated fine-tuning framework for Inkling that supports parameter-efficient adaptation. Meta's ecosystem (llama-factory, Axolotl) is more mature. Qwen has strong community tooling (Qwen-LoRA, Unsloth support).
Inkling vs Llama 4 vs Qwen 3.5: Best Use Cases for Each Model
Choose Inkling If:
- You need multimodal input (text + image + audio) in a single model
- You want Apache 2.0 purity with no use-case restrictions
- Your hardware budget favors MoE efficiency (41B active = lower inference cost)
- You need 1M context for massive document analysis
- You're building on the cutting edge and want first-mover advantage on a new architecture
Choose Llama 4 Maverick If:
- You need maximum raw performance and have the GPU budget
- You rely on Groq for ultra-low-latency inference
- You want the largest ecosystem — tools, community, cloud integrations
- You need Scout for single-GPU deployment (17B active)
Choose Qwen 3.5 If:
- You need 10M context for extreme long-document tasks
- Your primary workload is coding (HumanEval leader)
- You want lowest API cost at scale
- You need Chinese language excellence (Qwen's home turf)
- You prefer dense models over MoE for predictable latency
FAQ: Inkling vs Llama 4 vs Qwen 3.5 Comparison
What is Thinking Machines Inkling?
Inkling is a 975B parameter Mixture-of-Experts model (41B active) released by Mira Murati's Thinking Machines Lab in July 2026. It's Apache 2.0 licensed, multimodal (text, image, audio), and features a 1M token context window.
How does Inkling compare to Llama 4?
Inkling (41B active) is more hardware-efficient than Llama 4 Maverick (128B active) while matching or exceeding it on reasoning benchmarks (AIME 97.1% vs 96.4%, GPQA 87.2% vs 86.8%). Llama 4 has a larger ecosystem and Groq support. Inkling wins on licensing purity and multimodality.
Is Qwen 3.5 better than Llama 4?
On coding (HumanEval 92.4% vs 91.2%) and general knowledge (MMLU 89.5% vs 89.2%), Qwen 3.5 72B slightly edges out Llama 4 Maverick. Qwen also offers 10M context vs Llama 4's 10M (Scout) / 1M (Maverick). Llama 4 has better ecosystem and Groq inference.
What are the best open-weight AI models in 2026?
The top tier: Inkling (multimodal, Apache 2.0, MoE efficiency), Llama 4 Maverick/Scout (ecosystem, Groq), Qwen 3.5 (coding, 10M context, cost-effective). Honorable mentions: DeepSeek-V3, Nemotron 3 Ultra, Bonsai 27B (compression breakthrough).
Can I run Inkling on my own hardware?
Yes, with ~45 GB VRAM (INT4 quantized) or ~85 GB (FP8). That's 2× H100 80GB or 4× A100 80GB. Consumer GPUs (24-48 GB) can run heavily quantized versions with offloading, but expect slow inference.
Is Inkling open source?
Yes — Apache 2.0 license. The weights, architecture, and training code (partial) are publicly available on Hugging Face. This is the most permissive license among the three flagship models.
Conclusion: Inkling vs Llama 4 vs Qwen 3.5 — Three Kings of Open-Weight AI
The Inkling vs Llama 4 vs Qwen 3.5 comparison shows we've reached a new equilibrium in open-weight AI. No single model dominates across all dimensions. Inkling brings multimodal MoE efficiency and licensing purity. Llama 4 delivers ecosystem depth and Groq speed. Qwen 3.5 leads on coding, context length, and API cost.
For most teams, the decision comes down to: hardware budget (Inkling wins), ecosystem maturity (Llama 4 wins), or specific capability needs — coding and long context (Qwen 3.5), multimodal (Inkling), ultra-low latency (Llama 4 on Groq).
The Inkling vs Llama 4 vs Qwen 3.5 race just got more interesting — and that's a win for everyone building with AI. Read our earlier analysis of Grok 4.5: SpaceXAI's Latest Model and GLM 5.2: Chinese AI Model Review for more model comparisons.
Which open-weight model are you betting on for your next project — Inkling, Llama 4, or Qwen 3.5? Share your reasoning in the comments.
About the Author: Pruthviraj Khose is the founder and editor of markly.in, covering AI and technology with hands-on analysis of models, tools, and industry shifts.