Qwen3.5-122B-A10B vs DeepSeek V3.2: Efficiency vs Raw Power in Open-Weight AI

This is the matchup that tells you where the open-weight frontier is actually heading. On one side, DeepSeek V3.2 - 685 billion total parameters, 37 billion active per token, MIT licensed, backed by an API so cheap it forced the entire industry to reconsider their pricing. On the other side, Qwen3.5-122B-A10B - 122 billion total parameters, 10 billion active, Apache 2.0, and a benchmark profile that has no business being as strong as it is for a model this small.

The numbers tell an interesting story. DeepSeek V3.2 wins on raw benchmark ceilings: MMLU-Pro 85.0, Codeforces rating 2386, AIME 2025 score of 93.1. These are exceptional results. But Qwen3.5-122B-A10B fights back where you would not expect: GPQA Diamond 86.6 versus DeepSeek's 82.4, and SWE-bench Verified 72.0 versus DeepSeek's 67.8. The model activating 3.7x fewer parameters per token is winning on graduate-level reasoning and real-world software engineering.

The question is not which model is "better." It is whether you need raw power with a cheap API, or whether you need maximum intelligence per compute dollar on your own hardware.

Quick Comparison

Qwen3.5-122B-A10B: The Parameter Efficiency Play

The Qwen3.5-122B-A10B is doing something that should not work as well as it does. With only 10 billion active parameters per forward pass, it is operating at a compute budget roughly equivalent to a Llama 3.1 8B or a Mistral 7B. But its benchmark scores belong to a completely different tier.

The architecture is the key. Alibaba's Qwen team combined Gated Delta Networks with sparse Mixture-of-Experts routing, creating a hybrid system that is more selective about which parameters to activate and how information flows through the network. This is not just "we made a smaller MoE" - it is a fundamentally different approach to parameter utilization. The result is a model that extracts more intelligence per FLOP than anything else in the open-weight space right now.

On GPQA Diamond - the benchmark that tests graduate-level scientific reasoning with questions designed to fool non-experts - Qwen3.5-122B-A10B scores 86.6. DeepSeek V3.2, with 3.7x more active parameters, scores 82.4. That is a 4.2-point lead for the smaller model on one of the hardest reasoning benchmarks available. On SWE-bench Verified, which measures the ability to resolve real GitHub issues in actual codebases, Qwen scores 72.0 versus DeepSeek's 67.8 - a 4.2-point advantage that translates directly to practical software engineering capability.

The self-hosting story is where this efficiency really pays off. At FP8 quantization, the full 122B model fits in roughly 60-70 GB of VRAM. That is achievable on a dual RTX 5090 setup, a single A100 80GB, or an Apple Mac Studio with sufficient unified memory. You can serve this model from hardware that costs thousands of dollars, not tens of thousands. DeepSeek V3.2's 685B total parameters require an entirely different class of infrastructure - you are looking at multi-node GPU clusters. For more context on other efficient Qwen variants, see our coverage of the Qwen3.5 Flash and Qwen3.5-35B-A3B.

The main limitation is API availability. While the model is freely downloadable and self-hostable under Apache 2.0, there is no widely available third-party API ecosystem for it yet. Alibaba Cloud's Model Studio offers access, but with tiered pricing that is less transparent than DeepSeek's straightforward rate card. If you want to use this model, you are mostly committing to running it yourself.

DeepSeek V3.2: The Price-Performance King

DeepSeek V3.2 is the model that changed the economics of AI inference. When it launched with API pricing of $0.28 per million input tokens (cache miss) and $0.42 per million output tokens, it was not just cheaper than the competition - it was cheaper by an order of magnitude compared to frontier proprietary models. GPT-5 charges $1.25/$10.00 for the same token volumes. Claude Sonnet 4 charges $3/$15. DeepSeek is 95% cheaper than GPT-5 on output tokens.

And the performance justifies the price. On MMLU-Pro, DeepSeek V3.2 scores 85.0 - slightly below Qwen's 86.7, but still firmly in frontier territory. On competitive programming (Codeforces rating 2386) and mathematical reasoning (AIME 2025 score of 93.1), DeepSeek is among the best models available at any price point. The LiveCodeBench score of 74.1 and the Codeforces rating suggest a model that handles complex algorithmic reasoning at an elite level.

The architecture uses Multi-Latent Attention (MLA) with MQA mode for efficient key-value sharing, combined with DeepSeek Sparse Attention (DSA) for long-context processing. This is a model that was engineered from the ground up for inference efficiency at the API level, even though the raw parameter count is massive. The 37B active parameter count is large, but the attention mechanism innovations keep the actual FLOP cost lower than a naive dense model of similar size.

The MIT license is maximally permissive - identical in spirit to Qwen's Apache 2.0, both imposing essentially zero restrictions on commercial use. Where DeepSeek separates itself from Qwen is the API. DeepSeek runs its own inference infrastructure and offers direct API access that is reliable, well-documented, and absurdly cheap. The cache hit pricing of $0.028 per million tokens is effectively free for applications with repetitive prompt prefixes. New users get 5 million free tokens with no credit card required. For a deep dive into how DeepSeek performs in practice, see our DeepSeek V3.2 review.

The trade-offs are real, though. The 128K context window is notably shorter than Qwen's 262K. For most applications this is sufficient, but if you routinely process very long documents or maintain extended conversation histories, Qwen offers more room. Self-hosting is impractical for most teams - 685B parameters at FP8 is roughly 340 GB, requiring multiple enterprise GPUs. And while the API is cheap, you are dependent on DeepSeek's infrastructure availability, which has had capacity constraints during peak demand.

Benchmark Comparison

The pattern is clear: Qwen wins on reasoning-heavy benchmarks (GPQA, SWE-bench, IFEval) while DeepSeek wins on competitive math and programming (AIME, Codeforces, HumanEval). Both are strong on MMLU-Pro, with Qwen holding a slight edge.

What makes this interesting is the efficiency ratio. Qwen achieves these results with 3.7x fewer active parameters. If you normalize GPQA Diamond by active parameter count, Qwen is delivering 8.66 points per billion active parameters versus DeepSeek's 2.23. That is a 3.9x efficiency advantage on the hardest reasoning benchmark. Even on benchmarks where DeepSeek wins, the gap is rarely large enough to justify the 3.7x compute difference.

Pricing Analysis

This is where the comparison gets asymmetric. DeepSeek has a world-class API. Qwen has world-class self-hosting economics.

If you are calling an API, DeepSeek V3.2 is one of the cheapest frontier-quality options available. At $0.28 per million input tokens, you can process roughly 3.5 million tokens for one dollar. With cache hits, that drops to $0.028 - effectively 35 million tokens per dollar on input. No other model at this quality level comes close on per-token API cost.

If you are self-hosting, Qwen3.5-122B-A10B flips the equation. Once you own the hardware, inference is free. The 10B active parameter count means you can serve hundreds of requests per second on a single mid-range GPU node. Over the lifetime of a deployment, the total cost of ownership can be dramatically lower than paying per-token API fees, especially at high volume.

For teams processing fewer than a few million tokens per day, DeepSeek's API is probably cheaper than buying and maintaining inference hardware. For teams processing tens of millions of tokens daily, self-hosting Qwen could pay for itself within months. For the latest on how these models compare across the full open-source field, check our open-source LLM leaderboard.

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

Pros:

• 3.7x more parameter-efficient than DeepSeek V3.2 with competitive benchmarks
• Self-hostable on consumer/prosumer hardware (60-70 GB VRAM at FP8)
• GPQA Diamond 86.6 and SWE-bench 72.0 beat DeepSeek on reasoning tasks
• Apache 2.0 license - no restrictions whatsoever
• 262K context window (2x longer than DeepSeek's 128K)
• Hybrid Gated Delta Network + MoE architecture delivers novel efficiency gains
• Zero marginal inference cost once hardware is provisioned

Cons:

• No cheap, widely available third-party API (primarily Alibaba Cloud)
• Loses to DeepSeek on competitive math (AIME) and pure coding (HumanEval)
• Smaller community and ecosystem
• No built-in multimodal capabilities in this variant
• Self-hosting requires technical expertise and upfront hardware investment
• Limited independent benchmarking compared to DeepSeek

DeepSeek V3.2: Pros and Cons

Pros:

• API pricing is industry-leading ($0.28/$0.42 per million tokens)
• Cache hit pricing ($0.028 input) makes repetitive workloads nearly free
• Codeforces 2386 and AIME 93.1 - elite competitive programming and math
• MIT license - fully permissive
• Production-ready API with good documentation and reliability
• 5 million free tokens for new users with no credit card
• MMLU-Pro 85.0 confirms broad knowledge competency

Cons:

• 685B total parameters makes self-hosting impractical for most teams
• 37B active parameters means 3.7x higher per-token compute than Qwen
• GPQA Diamond 82.4 trails Qwen's 86.6 on hard reasoning
• SWE-bench 67.8 trails Qwen's 72.0 on real-world coding
• 128K context window is shorter than most competitors
• API capacity has been constrained during peak demand periods
• Dependent on DeepSeek's infrastructure for API access

Verdict

Choose Qwen3.5-122B-A10B if you are building a self-hosted deployment and want the maximum reasoning and coding capability per FLOP. The 10B active parameter count is not just a spec sheet number - it translates directly to lower hardware costs, higher throughput, and more accessible infrastructure. If you are a startup, a research lab, or an individual developer who wants frontier-class reasoning without paying per token, this is the model to run. For related Qwen models worth exploring, see our coverage of Qwen3.5-27B and a broader take in our Qwen 3 review.

Choose DeepSeek V3.2 if you want to call an API and pay by the token at the lowest cost in the industry. The pricing is not a gimmick - it is a genuine structural advantage backed by a well-engineered inference stack. If your workload involves heavy math, competitive programming, or high-volume API calls where self-hosting is impractical, DeepSeek gives you frontier performance at a fraction of what any competitor charges. It is also the better choice if you need a turnkey solution without managing GPU infrastructure.

Choose either if you are benchmarking open-weight models for a new project. Both represent the cutting edge of what is available outside the proprietary frontier. Run your actual prompts through both and measure what matters for your use case. The benchmarks say Qwen is more efficient and DeepSeek is cheaper via API - but your workload may weight different benchmarks than the ones published. For a broader perspective on how these models rank, see our coding benchmarks leaderboard.

Sources

• Qwen3.5-122B-A10B - Hugging Face
• Qwen3.5 Official Blog
• DeepSeek V3.2 Technical Report
• DeepSeek API Pricing
• DeepSeek V3.2 - NVIDIA NIM
• Open Source LLM Leaderboard 2026 - VERTU
• LLM Benchmarks 2026 - llm-stats.com
• DeepSeek V3.2 Analysis - MGX