Qwen3.5-122B-A10B vs Mistral Large 3: When 4x More Parameters Buys You Less

Both of these models ship under Apache 2.0. Both use Mixture-of-Experts architectures. Both target the open-weight frontier. And yet, the gap between them on certain benchmarks is so large that you would assume they belong to different generations of AI development.

Mistral Large 3 is the bigger model by a significant margin: roughly 675 billion total parameters with about 40 billion active per token. Qwen3.5-122B-A10B has 122 billion total and activates just 10 billion. Mistral activates 4x more parameters on every single forward pass. By any reasonable prior, it should be the stronger model.

On GPQA Diamond, Qwen scores 86.6. Mistral scores 43.9. That is not a competitive gap - that is a 42.7-point chasm. On one of the most respected graduate-level reasoning benchmarks available, the model with 4x fewer active parameters scores nearly double. This is the kind of result that makes you question whether raw parameter count means anything at all for reasoning capability, or whether architecture and training methodology have become the dominant factors.

The story is more nuanced than "Qwen wins everything," of course. Mistral Large 3 has a strong LMArena ELO of 1418, built-in vision capabilities, a 256K context window, and Mistral's EU compliance positioning that matters for certain enterprise buyers. But the benchmark picture demands an honest accounting.

Quick Comparison

Qwen3.5-122B-A10B: Architecture Over Brute Force

There is a reason I keep coming back to the active parameter count when discussing this model: 10 billion. That is the compute footprint that Qwen3.5-122B-A10B uses to beat models four to six times its size on reasoning benchmarks. The hybrid architecture - Gated Delta Networks combined with sparse MoE routing - represents a genuine step forward in how efficiently a model can allocate computation.

On MMLU-Pro, Qwen scores 86.7 versus Mistral's approximately 73.1. That is a 13.6-point lead. On GPQA Diamond, the gap widens to an almost absurd 42.7 points (86.6 vs 43.9). These are not cherry-picked metrics - MMLU-Pro and GPQA Diamond are two of the most widely cited benchmarks in the industry, testing broad knowledge and deep scientific reasoning respectively. A model scoring 43.9 on GPQA Diamond is performing at a level that suggests fundamental limitations in reasoning depth, regardless of how many parameters it has.

The SWE-bench Verified score of 72.0 adds another dimension. This benchmark measures practical software engineering - can the model resolve real GitHub issues by reading code, understanding context, and generating correct patches? A score of 72.0 places Qwen in competitive territory with models that cost orders of magnitude more to run. Mistral Large 3 has not published an official SWE-bench Verified score, but independent testing places it in a lower tier on code-heavy benchmarks.

Self-hosting economics favor Qwen overwhelmingly. At FP8 quantization, the 122B total parameter model fits in roughly 60-70 GB of VRAM. A pair of RTX 5090s, a single A100 80GB, or a well-configured Mac Studio can handle it. Mistral Large 3's approximately 675B total parameters require around 340 GB of VRAM at FP8 - that is four to eight enterprise GPUs depending on the configuration. The 4x active parameter difference translates directly to 4x higher per-token compute costs during inference. For a broader perspective on Qwen's position in the open-weight ecosystem, check our open-source LLM leaderboard.

The context window at 262K tokens is comparable to Mistral's 256K - essentially a tie for most practical purposes. Both can handle large codebases, long documents, and extended conversations without truncation issues.

Mistral Large 3: The Enterprise European Play

Mistral Large 3 is not a model you evaluate purely on benchmarks. Mistral AI has positioned it as an enterprise-grade, EU-sovereign AI system, and for certain buyers, that framing matters more than any GPQA score.

The company is headquartered in Paris. The model was trained and can be served from European data centers. It is designed to comply with the EU AI Act, and Mistral has built relationships with European government agencies, financial institutions, and regulated industries that have compliance requirements around data sovereignty. If you are a European bank, a healthcare provider subject to GDPR, or a government contractor that needs to demonstrate EU-based AI processing, Mistral's compliance story is a genuine differentiator that Alibaba cannot replicate.

The LMArena ELO of 1418 deserves attention. This human preference ranking placed Mistral Large 3 at number 2 among non-reasoning open-source models at launch. ELO measures something different from academic benchmarks - it captures conversational quality, instruction following, tone, helpfulness, and the subjective experience of interacting with the model. A model can score poorly on GPQA Diamond (which tests narrow expert-level reasoning) while still being excellent at general conversation, customer support, creative writing, and the broad category of tasks that most production deployments actually involve.

The vision capabilities are native, not bolted on. Mistral Large 3 can process images alongside text, handle visual question answering, analyze charts and diagrams, and perform document OCR. Qwen3.5-122B-A10B, in its current variant, is text-only. If your application involves any visual input, this comparison is over before it starts.

The API is straightforward: $0.50 per million input tokens, $1.50 per million output tokens. This is more expensive than DeepSeek V3.2's pricing but significantly cheaper than proprietary alternatives like Claude or GPT-5. Mistral also offers deployment through major cloud providers (AWS, Azure, GCP) and through its own La Plateforme API. The 256K context window with 16K maximum output tokens covers most production use cases comfortably.

Where Mistral falls down is on the benchmarks that test the hardest reasoning tasks. The GPQA Diamond score of 43.9 is not competitive with the current frontier. The MMLU-Pro score around 73.1 is solid but not exceptional. On coding benchmarks, HumanEval at 90.2 is strong, but more demanding evaluations like LiveCodeBench and SWE-bench show the model sitting in a middle tier rather than at the top. Mistral has historically prioritized breadth and practical utility over benchmark maximization, and the scores reflect that trade-off.

Benchmark Comparison

The benchmark table reveals two models with completely different strengths. Qwen dominates every text reasoning and coding benchmark by margins that range from significant (MMLU +2.5) to extraordinary (GPQA +42.7). Mistral wins on HumanEval, holds a strong human preference ranking, and offers multimodal capabilities that Qwen lacks entirely.

The GPQA gap requires comment because it is so large. A 42.7-point difference on a 0-100 scale is not normal. It suggests that Mistral Large 3's architecture, despite its massive parameter count, does not translate well to the kind of deep, step-by-step scientific reasoning that GPQA Diamond tests. This could be a training data issue, an architecture limitation, or a deliberate trade-off by Mistral to optimize for other capabilities. Whatever the cause, it is the single most striking data point in this comparison.

Mistral's HumanEval advantage (90.2 vs ~84.8) is notable but narrower, and HumanEval is increasingly considered a solved benchmark - models are approaching ceiling performance, and the remaining differences may not be practically meaningful. More demanding coding evaluations like SWE-bench and LiveCodeBench tell a different story.

Pricing Analysis

Both models are available via API and as downloadable weights. The pricing structures differ significantly.

Mistral's API pricing at $0.50/$1.50 per million tokens is transparent and competitive for its class. It is more expensive than DeepSeek V3.2 ($0.28/$0.42) but cheaper than most proprietary alternatives. The broad cloud provider availability - AWS Bedrock, Azure AI, Google Cloud, plus Mistral's own platform - gives enterprise buyers deployment flexibility that Qwen currently lacks.

Qwen's value proposition is self-hosting. The 4x active parameter advantage means 4x lower per-token inference compute. If you are running sustained workloads, the hardware investment in a Qwen-capable node pays for itself rapidly compared to per-token API fees. The Apache 2.0 license on both models means there are no commercial licensing gotchas - the choice is purely economic and technical.

For organizations that need EU data residency and Mistral's compliance story, the pricing premium is simply the cost of regulatory compliance. You cannot self-host Qwen in an EU data center and claim the same regulatory positioning that Mistral offers out of the box. That premium has real value for the right buyer.

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

Pros:

• GPQA Diamond 86.6 - nearly double Mistral's 43.9 on graduate-level reasoning
• MMLU-Pro 86.7 - 13.6-point lead over Mistral on broad knowledge
• Only 10B active parameters means 4x lower per-token compute than Mistral
• Self-hostable on consumer hardware (60-70 GB VRAM at FP8)
• SWE-bench Verified 72.0 demonstrates strong practical coding ability
• Apache 2.0 license with zero restrictions
• Hybrid Gated Delta Network + MoE architecture is novel and efficient
• 262K context window matches Mistral's 256K

Cons:

• No multimodal capabilities (text only in this variant)
• No EU compliance story or European data sovereignty positioning
• Limited API provider ecosystem compared to Mistral's cloud partnerships
• No LMArena ELO ranking yet - human preference data unavailable
• Smaller Western developer community and documentation
• HumanEval score (~84.8) trails Mistral's 90.2

Mistral Large 3: Pros and Cons

Pros:

• LMArena ELO 1418 - second among open-source non-reasoning models at launch
• Native vision capabilities for multimodal workloads
• EU-headquartered with compliance positioning for regulated industries
• HumanEval 90.2 - strong function-level code generation
• Broad cloud provider availability (AWS, Azure, GCP, La Plateforme)
• Apache 2.0 license - same permissiveness as Qwen
• 256K context window with good long-context handling
• Established enterprise relationships and support channels

Cons:

• GPQA Diamond 43.9 - a 42.7-point deficit to Qwen on hard reasoning
• MMLU-Pro ~73.1 is not competitive with current frontier models
• ~40B active parameters means 4x higher inference cost than Qwen's 10B
• ~675B total parameters makes self-hosting extremely expensive
• API pricing ($0.50/$1.50) is higher than DeepSeek and Qwen self-hosting
• LiveCodeBench and SWE-bench scores place it in the middle tier for coding
• The gap between conversational quality (ELO) and reasoning benchmarks raises questions about depth

Verdict

Choose Qwen3.5-122B-A10B if your workload is text reasoning, code generation, or any task where benchmark performance on GPQA, MMLU-Pro, and SWE-bench matters. The efficiency numbers are not marketing - they represent a fundamental architecture advantage that translates to lower hardware costs and higher throughput. If you are a developer, researcher, or startup that wants to self-host a frontier-class reasoning model without enterprise GPU infrastructure, this is the model. For even smaller options in the Qwen family, see our pages on Qwen3.5-35B-A3B and Qwen3.5-27B.

Choose Mistral Large 3 if you are an enterprise buyer in a regulated industry, particularly in Europe. The EU compliance positioning, the vision capabilities, the cloud provider partnerships, and the strong conversational ELO make it a compelling choice for customer-facing applications where regulatory compliance and multimodal support matter more than benchmark scores on academic reasoning tests. The API is well-priced for its segment, and the 256K context window handles most enterprise workloads. For a broader look at how Mistral compares across its full model lineup, see our Mistral Large 3 page.

Choose either if you are evaluating Apache 2.0 licensed MoE models for a new project and want to understand the architectural trade-offs between parameter count and reasoning efficiency. These models represent two very different philosophies - Mistral bet on scale and breadth, Qwen bet on architectural innovation and efficiency - and the benchmarks show which bet paid off for which capability. Run them both. For a wider view of the open-source landscape, check our open-source LLM leaderboard and our guide to open-source vs proprietary AI.

Sources

• Qwen3.5-122B-A10B - Hugging Face
• Qwen3.5 Official Blog
• Mistral Large 3 - IntuitionLabs
• Mistral Large 3 Review - Medium
• Mistral AI Pricing
• Mistral Large 3 - Artificial Analysis
• Mistral Docs - Benchmarks
• LLM Benchmarks 2026 - llm-stats.com
• Open Source LLM Leaderboard 2026 - VERTU