Qwen3.5-122B-A10B vs Llama 4 Maverick: The Efficiency Gap Nobody Expected

On paper, Llama 4 Maverick should win this comparison. It has 400 billion total parameters, 128 experts, native multimodal support, and Meta's enormous engineering budget behind it. Qwen3.5-122B-A10B has 122 billion total parameters and activates just 10 billion per token. It is a fraction of the size.

And yet, when you look at the benchmarks, the smaller model wins on nearly every text metric that matters. This is not a minor gap. On GPQA Diamond - one of the hardest reasoning benchmarks available - Qwen scores 86.6 versus Maverick's 69.8. That is a 16.8-point spread. On MMLU-Pro, Qwen leads by 6.2 points. The model with 3.3x fewer total parameters and 1.7x fewer active parameters is delivering better results on the benchmarks that actually test reasoning depth.

That deserves a closer look.

Quick Comparison

Qwen3.5-122B-A10B: The Efficiency Machine

Alibaba's Qwen team has been on a tear. The Qwen3.5 family launched with models ranging from 27B dense to 397B MoE, but the 122B-A10B variant is the one that caught my attention. It uses a hybrid architecture combining Gated Delta Networks with sparse Mixture-of-Experts, which is a departure from the standard transformer-only MoE designs that most competitors use.

The headline number is 10 billion active parameters per token. For context, that is roughly the compute footprint of a Llama 3.1 8B model, but delivering benchmark scores that compete with models activating 3-4x more parameters. On MMLU-Pro, it scores 86.7 - a number that puts it ahead of not just Maverick, but ahead of several models that are dramatically larger. On GPQA Diamond, its 86.6 score is competitive with the best reasoning models available, including some proprietary ones.

The practical implication is that this model can run on hardware that would choke on Maverick. The 122B total parameter count means the full model fits in roughly 60-70 GB of VRAM at FP8 quantization, and the 10B active parameter count keeps inference throughput high. If you are running a DGX Spark, an RTX 5090 pair, or even a well-configured Mac Studio, this model is within reach. Maverick's 400B total parameters are a different story entirely - you are looking at multi-GPU setups or dedicated inference clusters.

The context window is 262,144 tokens natively, extendable to over a million with rope scaling. That is generous, though it falls short of Maverick's 1M native and 10M extended claim. For most practical workloads - code repositories, document analysis, long conversations - 262K is more than sufficient. If you need to process an entire book or a massive codebase in a single pass, Maverick has the advantage.

The license is Apache 2.0, which is as permissive as it gets. No usage restrictions, no mandatory attribution for commercial deployments, no geographic limitations. This matters if you are building products on top of the model. For more on how Qwen fits into the broader open-source landscape, see our open-source vs proprietary AI guide.

Llama 4 Maverick: Meta's Multimodal Play

Maverick is Meta's first MoE release, and it is ambitious. The 128-expert architecture with 17B active parameters is designed to maximize capability while keeping per-token compute manageable. The total parameter count of 400B means the model has enormous capacity for storing knowledge and patterns, even though only a fraction fires on any given token.

The multimodal story is Maverick's clearest differentiator. This is not a bolted-on vision encoder - the model was trained from the ground up to process images and text together. Meta's blog describes it as "natively multimodal," and the benchmarks support this: Maverick beats GPT-4o and Gemini 2.0 Flash on multimodal tasks. If your workload involves image understanding, visual question answering, or document OCR, Qwen3.5-122B-A10B simply cannot compete because it does not have vision capabilities in this variant.

The Chatbot Arena ELO of 1417 is worth discussing. This is a human preference ranking, not an academic benchmark, and it measures something fundamentally different from MMLU-Pro or GPQA. It reflects how well the model handles open-ended conversation, instruction following, and the subjective qualities that make a model "feel" good to use. Maverick placed second only to Gemini 2.5 Pro among non-reasoning models at launch - a strong result. Qwen3.5-122B-A10B does not yet have an official Arena ranking, so a direct comparison on this axis is not possible.

Meta's context window claims are eye-catching: 1M tokens native, extendable to 10M. That is the longest context of any open-weight model. Whether it maintains quality at those extreme lengths is a different question - independent testing of very long context performance has been mixed for most models - but the architectural support is there. If you are working with extremely long documents or need to hold massive conversation histories, this is a genuine advantage.

The licensing is more restrictive than Qwen's Apache 2.0. Llama's community license includes usage restrictions for applications with over 700 million monthly active users and requires compliance with Meta's acceptable use policy. For most developers and companies, this is not a practical concern. For very large deployments, it is worth reading the fine print.

Benchmark Comparison

The benchmark picture is lopsided in Qwen's favor on text tasks. The GPQA gap of 16.8 points is enormous - that benchmark tests graduate-level science and reasoning, and a spread that wide usually separates model generations, not peer competitors. The LiveCodeBench gap is even more striking, though Maverick's 43.4 may reflect an older evaluation window and Meta's focus on multimodal rather than coding optimization.

Where Maverick wins is on dimensions that benchmarks do not fully capture: multimodal capability, human preference, and context length. These are not minor considerations. If your application needs any of them, the benchmark deltas on MMLU-Pro and GPQA become secondary.

Pricing Analysis

Qwen3.5-122B-A10B is primarily a self-host model. Alibaba Cloud offers API access through Model Studio, but the model's real value proposition is running it on your own hardware. The 10B active parameter count makes inference extremely efficient - you can serve this model on a single high-end GPU at reasonable throughput. The Apache 2.0 license means no per-token fees, no API rate limits, and no vendor lock-in.

Llama 4 Maverick is available through a wide range of API providers. Median pricing sits around $0.31 per million input tokens and $0.85 per million output tokens, though deals can be significantly cheaper. DeepInfra offers it at $0.15 per million input tokens. Groq charges $0.50 input / $0.77 output per million tokens. If you want to self-host Maverick, you need substantially more hardware - the 400B total parameters require multi-GPU configurations that most individual developers cannot justify.

For budget-conscious deployments, Qwen is the clear winner. The hardware requirements are dramatically lower, and the Apache 2.0 license eliminates any commercial licensing concerns.

Qwen3.5-122B-A10B: Pros and Cons

Pros:

• Best-in-class reasoning benchmarks for its parameter class (GPQA 86.6, MMLU-Pro 86.7)
• Only 10B active parameters - runs on consumer-grade multi-GPU setups
• Apache 2.0 license with zero commercial restrictions
• 122B total parameters means practical self-hosting without enterprise hardware
• Hybrid Gated Delta Network + MoE architecture is genuinely novel
• Strong coding performance (SWE-bench 72.0, LiveCodeBench ~78.0)

Cons:

• No multimodal capabilities in this variant (text only)
• Limited API provider ecosystem compared to Llama
• No Chatbot Arena ranking yet, so human preference data is missing
• Alibaba Cloud API pricing is opaque with tiered structures
• Smaller community and ecosystem than Meta's Llama family
• Context window (262K) is shorter than Maverick's 1M native

Llama 4 Maverick: Pros and Cons

Pros:

• Native multimodal support (vision + text) trained from the ground up
• Strong Chatbot Arena ELO of 1417 indicates excellent conversational quality
• 1M native context window, extendable to 10M - longest of any open model
• Broad API provider availability (Together AI, Fireworks, Groq, DeepInfra, etc.)
• Large community and ecosystem backed by Meta
• 128-expert MoE architecture with proven scaling properties

Cons:

• Text benchmarks significantly trail Qwen (GPQA 69.8 vs 86.6)
• 400B total parameters makes self-hosting expensive and impractical for most
• Llama Community License is more restrictive than Apache 2.0
• 17B active parameters means higher per-token inference cost than Qwen's 10B
• Benchmark controversy at launch (Artificial Analysis initially could not reproduce claimed scores)
• Coding benchmarks (LiveCodeBench 43.4) lag behind specialized models

Verdict

Choose Qwen3.5-122B-A10B if you are building text-focused applications - coding assistants, reasoning engines, document analysis, knowledge retrieval - and want the best possible benchmark performance per dollar of inference cost. The 10B active parameter count is a genuine breakthrough in efficiency, and the Apache 2.0 license removes every commercial barrier. This is the model for self-hosters and teams that want maximum control. For more on how it stacks up against other small but powerful Qwen models, see our pages on Qwen3.5-35B-A3B and Qwen3.5-27B.

Choose Llama 4 Maverick if your workload requires multimodal input (images + text), you need the longest possible context window, or you value human preference metrics over academic benchmarks. The API ecosystem is mature, pricing is competitive, and Meta's ongoing investment in the Llama family means continued updates. It is also the safer choice if you want broad third-party support without managing your own infrastructure.

Choose either if you are evaluating open-weight models for a new project and want to test both. They are different enough that the right choice depends entirely on your use case. Run them both on your actual workload. The benchmarks tell a story, but your data tells the one that matters. For a broader view of how these models rank against all open-source competitors, check our open-source LLM leaderboard.

Sources

• Qwen3.5-122B-A10B - Hugging Face
• Qwen3.5 Official Blog
• Llama 4 - Meta AI Blog
• Llama 4 Maverick - Artificial Analysis
• Llama 4 Maverick API Providers - Artificial Analysis
• Open Source LLM Leaderboard 2026 - VERTU
• LLM Benchmarks 2026 - llm-stats.com