Qwen3.5-35B-A3B vs Llama 4 Scout: 3B Active Parameters vs 17B - Does 5.7x More Compute Actually Win?

This comparison should not even be close. Llama 4 Scout activates 17 billion parameters per token - 5.7 times more compute than Qwen3.5-35B-A3B's 3 billion. It has 109B total parameters versus 35B. It is backed by Meta, trained on 40 trillion tokens with 5 million GPU hours of H100 compute. By every resource metric, Scout should dominate.

It does not. Qwen3.5-35B-A3B posts MMLU-Pro 85.3 versus Scout's 74.3. GPQA Diamond 84.2 versus 57.2. SWE-bench Verified 69.2 versus Scout's estimated low-30s range. The model activating one-sixth the parameters is winning by double-digit margins on the benchmarks that matter for reasoning and coding.

But before you write off Scout entirely, consider this: it has a 10 million token context window - 38x longer than Qwen's 262K native context. It processes images natively through early fusion. And it runs on the Meta ecosystem, which means day-one support across every major inference framework, cloud provider, and deployment platform. Sometimes the numbers that do not show up on benchmark tables matter more than the ones that do.

Quick Comparison

Qwen3.5-35B-A3B: Efficiency Taken to Its Logical Extreme

Qwen3.5-35B-A3B is arguably the most efficient language model ever released in terms of benchmark-score-per-active-parameter. At 3B active parameters, it surpasses the previous Qwen3-235B-A22B flagship - a model that activated 7.3x more parameters - across nearly every benchmark. When you compare it to Scout's 17B active, the efficiency gap becomes almost absurd.

The architecture makes this possible. The Gated DeltaNet hybrid uses 256 experts (the highest count in any production MoE model) with only 8 routed plus 1 shared active per token. The 3:1 ratio of DeltaNet layers to full attention layers means most computation happens at linear cost. The training used multi-step token prediction (MTP), and the model is natively multimodal - early fusion on text, image, and video data from scratch.

The benchmark numbers speak for themselves. MMLU-Pro at 85.3 versus Scout's 74.3 is an 11-point gap in Qwen's favor despite Scout using 5.7x more compute per token. On GPQA Diamond - graduate-level reasoning - the gap widens to 27 points (84.2 vs 57.2). These are not marginal differences. They represent a generational improvement in how efficiently parameters can be utilized.

For coding, Qwen posts SWE-bench Verified 69.2% and LiveCodeBench v6 74.6%. Scout's LiveCodeBench sits at 32.8% - it actually regressed below the older Llama 3.3 70B on code generation. For anyone building coding agents or automated code repair pipelines, this comparison is not close. The agentic benchmarks reinforce the pattern: Qwen's TAU2-Bench 81.2 and AndroidWorld 71.1 are in a different league from what Scout offers. See the Coding Benchmarks Leaderboard for the full picture.

The model ships under Apache 2.0 - the most permissive license available. Quantized versions run on consumer GPUs. The managed API through Qwen3.5-Flash provides production-grade hosting at $0.10/M tokens. For the full Qwen 3.5 family, see also Qwen3.5-122B-A10B and Qwen3.5-27B.

Llama 4 Scout: When Context and Ecosystem Trump Benchmarks

Reading the benchmark comparison above, you might wonder why anyone would choose Scout. The answer is that benchmark tables do not capture everything that matters in production deployment. Scout has three genuine advantages that no amount of MMLU-Pro points can substitute for.

First, the 10 million token context window. This is not marketing vaporware - Meta claims perfect needle-in-a-haystack retrieval across the entire 10M window. Qwen's native context is 262K, extensible to approximately 1M with YaRN scaling. Scout's window is 10x longer at minimum, 38x longer compared to native context. For workloads that involve processing entire codebases, legal document sets, book-length manuscripts, or multi-year conversation histories, Scout offers a capability that Qwen simply cannot match. No benchmark captures the value of being able to reason across 10 million tokens of context.

Second, native multimodal with a focus on document understanding. Both models handle images, but Scout's early-fusion approach was trained on 40 trillion tokens of text, image, and video data. The vision benchmarks reflect this: DocVQA at 94.4%, ChartQA at 90.0%, MMMU at 73.4%. Qwen is strong on vision too (MMMU 81.4%, which actually beats Scout), but Scout's document understanding numbers are production-ready. If your use case is analyzing PDFs, receipts, charts, or screenshots at scale, Scout is battle-tested.

Third, the Meta ecosystem. Scout launched in April 2025 - nearly a year ago - which means it has had time to mature across inference frameworks, cloud providers, and deployment tooling. Every major platform supports it: vLLM, SGLang, TensorRT-LLM, Ollama, HuggingFace, DeepInfra, Lambda, Groq, SambaNova, AWS, Azure, GCP. Qwen3.5-35B-A3B's Gated DeltaNet architecture launched days ago and framework support is still catching up. If you need to deploy to production this week with your existing infrastructure, Scout has far less friction.

The weaknesses are just as clear. Scout's knowledge cutoff is August 2024 - over 18 months old. The Llama 4 Community License caps commercial use at 700M monthly active users. And LiveCodeBench at 32.8% is below the older Llama 3.3 70B's 33.3% - an embarrassing regression for a newer, larger model.

Detailed Benchmark Comparison

The benchmark story is lopsided in Qwen's favor on reasoning and coding, but the margins vary in interesting ways. GPQA Diamond shows the largest gap: 27 points. This is a graduate-level reasoning benchmark where Scout's 57.2% is mediocre by current standards - it suggests that Scout's architecture, despite its massive parameter count, was not optimized for this kind of deep analytical reasoning.

The coding gap is even more dramatic. LiveCodeBench 74.6 vs 32.8 is a 41.8-point difference. SWE-bench is harder to compare directly because Scout does not have a widely-cited score, but extrapolating from its LiveCodeBench performance and its positioning below Llama 3.3 70B on code generation, it is likely in the low-to-mid 30s range. This means Qwen fixes roughly twice as many real-world GitHub issues per attempt. For any coding-adjacent use case, the comparison is not competitive.

Scout wins on document understanding (DocVQA 94.4% is genuinely excellent), multilingual capability (90.6% MGSM across languages), and raw context length. These are specialized advantages, but they are real. A model that can process 10M tokens reliably is useful for applications that a 262K-context model simply cannot address, regardless of how smart it is per-token.

It is worth noting the 10-month age gap between these models. Scout launched April 2025, Qwen launched February 2026. In the current pace of AI development, ten months is an eternity. Many of Qwen's advantages reflect the rapid progress in training efficiency and architecture design that happened over that period rather than any fundamental superiority. See our Open Source LLM Leaderboard for how both models rank in the current landscape.

Pricing and Deployment

The deployment economics tell a story of their own. Qwen's 35B total parameters in BF16 need about 70GB VRAM - tight for a single GPU but doable on an H100-80GB. Scout's 109B total parameters need approximately 218GB in BF16, which means multi-GPU deployment is mandatory at full precision. With INT4 quantization, both fit on a single GPU (20GB and 27GB respectively), though Scout's quantized performance may degrade more since you are compressing a larger model.

On API pricing, Scout has a slight edge on input tokens ($0.08/M vs $0.10/M through their respective cheapest providers), but Qwen is cheaper on output ($0.10/M vs $0.30/M). For workloads that generate significant output - code generation, long-form writing, agent chains - Qwen's 3x cheaper output pricing adds up. The total cost per useful output is even more tilted toward Qwen when you factor in quality: if Qwen solves a coding problem in one pass that takes Scout three attempts (based on the benchmark ratios), the effective cost difference is even larger.

Where Scout has an unambiguous deployment advantage is framework maturity. Every major inference framework, cloud provider, and deployment tool supports Scout out of the box. Qwen's Gated DeltaNet architecture is days old and support is still being added. If you are a team with existing infrastructure, Scout is easier to slot into production today.

On licensing, Apache 2.0 (Qwen) means no restrictions beyond attribution. The Llama 4 Community License (Scout) adds a 700M monthly active user cap and terms about model derivatives. For a deeper discussion, see our open source vs proprietary AI guide.

Pros and Cons

Qwen3.5-35B-A3B

Pros:

• 3B active parameters post frontier-class scores - the most efficient model-per-parameter in production
• MMLU-Pro 85.3, GPQA 84.2 - 11 and 27 points above Scout on knowledge and reasoning
• SWE-bench 69.2% and LiveCodeBench 74.6% - coding benchmarks that are not in the same tier as Scout
• TAU2-Bench 81.2 and AndroidWorld 71.1 - best-in-class agentic performance at any parameter count
• Apache 2.0 license with 201 language support and native video understanding
• Consumer GPU deployment via quantization - lower VRAM than Scout (20GB vs 27GB Q4)

Cons:

• 262K native context (1M extended) is a fraction of Scout's 10M - not viable for extreme-context workloads
• Gated DeltaNet framework support is days old - production deployment friction today
• 256-expert MoE requires careful quantization to maintain quality
• No established ecosystem comparable to Meta's Llama infrastructure
• Self-reported benchmarks pending third-party verification
• Knowledge cutoff details not yet published

Llama 4 Scout

Pros:

• 10 million token context window - 38x longer than Qwen's native context, unmatched in open-weight models
• DocVQA 94.4% and ChartQA 90.0% - production-grade document understanding
• Mature ecosystem - day-one support across all major frameworks and cloud providers
• Competitive API pricing ($0.08/M input) with multiple providers and throughput options
• Native multimodal through early fusion - architecturally clean image understanding
• 10 months of production hardening, community tooling, and bug fixes

Cons:

• MMLU-Pro 74.3 and GPQA 57.2 are significantly behind Qwen despite using 5.7x more active parameters
• LiveCodeBench 32.8% actually regresses below Llama 3.3 70B - coding is a genuine weakness
• Knowledge cutoff of August 2024 is over 18 months stale
• 109B total parameters mean higher deployment costs for self-hosting (multi-GPU BF16)
• Llama 4 Community License is not truly open - 700M MAU cap, attribution required
• No video understanding (image only, vs Qwen's text+image+video)

Verdict

This is not a David-vs-Goliath story with a clean ending. David wins on the benchmarks, convincingly. But Goliath has a 10-million-token context window that David cannot touch, and a deployment ecosystem that David has not had time to build.

Choose Qwen3.5-35B-A3B if your workload is accuracy-bound - coding agents, knowledge Q&A, reasoning tasks, agentic workflows, or anything where per-request quality drives the outcome. The benchmark gaps are not marginal; they are generational. A 27-point GPQA lead and a doubled SWE-bench score mean fundamentally different user experiences. At 3B active parameters, it is also cheaper to run for equivalent-length requests. If you are starting a new project today and can tolerate the framework maturity gap, Qwen is the better model by almost every measure that shows up on a benchmark table.

Choose Llama 4 Scout if your workload is context-bound or ecosystem-bound. If you need to process 10 million tokens in a single pass - entire codebases, multi-year document archives, book-length inputs - Scout is the only open-weight option. If you need document analysis (DocVQA 94.4%), image understanding, and broad multilingual support with mature deployment tooling, Scout delivers. And if you need to deploy to production this week with your existing infrastructure, Scout's 10-month ecosystem advantage is real.

Choose either if you need native multimodal capability from an open-weight model. Both handle images, both are commercially licensed, both run on a single GPU with quantization. The engineering decision comes down to whether your bottleneck is intelligence per token (Qwen) or tokens per context window (Scout). For most workloads in early 2026, intelligence per token is the binding constraint - but the exceptions are real and growing.

Sources:

• Qwen3.5-35B-A3B Model Card (HuggingFace)
• Qwen3.5: Towards Native Multimodal Agents (Blog)
• Llama-4-Scout-17B-16E Model Card (HuggingFace)
• The Llama 4 Herd (Meta AI Blog)
• Llama 4 Scout Pricing and Benchmarks (llm-stats.com)
• Llama 4 Scout Provider Analysis (Artificial Analysis)
• Llama 4 Features and Benchmarks (Llama.com)
• Qwen3.5 Developer Guide (NxCode)