Qwen3.5-Flash vs DeepSeek V3.2: Budget API Battle With a Pricing Twist

The budget API tier used to be boring. You picked GPT-4o mini, paid your $0.15/$0.60, and moved on. In 2026, two open-weight models have demolished that pricing floor and made the choice genuinely interesting: Qwen3.5-Flash from Alibaba and DeepSeek V3.2 from DeepSeek.

On paper, this looks like a simple comparison. Qwen charges a flat $0.10/$0.40 per million tokens. DeepSeek charges $0.028/$0.42 on cache hits and $0.28/$0.42 on cache misses. Both are open-weight models backed by well-funded Chinese AI labs. Both deliver benchmark scores that would have been considered frontier-class in early 2025.

But the pricing structure creates fundamentally different economics. DeepSeek is 3.5x cheaper than Qwen on input - if and only if you hit the cache. Miss the cache, and DeepSeek is 2.8x more expensive on input than Qwen. That conditional pricing means your effective cost depends on your workload pattern, not just your volume. Meanwhile, Qwen offers something DeepSeek does not: a 1M-token context window versus 128K, and predictable flat pricing you can model in a spreadsheet without knowing your cache hit rate.

This is not a "one model is better" comparison. It is a "your workload determines which model saves you money" comparison, and the benchmark differences add another layer of nuance.

Quick Comparison

Qwen3.5-Flash: Flat Pricing and Long Context

Qwen3.5-Flash aligns with the Qwen3.5-35B-A3B open-weight model - 35 billion total parameters, 3 billion active per token, built on Alibaba's Gated DeltaNet architecture combined with mixture-of-experts. The production API adds features the open weights do not include: a 1M-token context window (versus 262K native for the base model), built-in tool calling, context caching, and a toggleable thinking mode.

The benchmark profile is strong across the board. MMLU-Pro at 85.3, GPQA Diamond at 84.2, SWE-bench Verified at 69.2, and LiveCodeBench v6 at 74.6 place it firmly in what I would call the "upper mid-tier" - not quite matching the very best frontier models, but comfortably ahead of the budget class. The Codeforces rating of 2028 indicates competitive programming capability, and the IFEval score of 91.9 suggests reliable instruction following for structured tasks. See how these numbers stack up against the full open-source landscape on our open-source LLM leaderboard.

Where Qwen3.5-Flash has a structural advantage over DeepSeek V3.2 is the context window. At 1M tokens versus 128K, Qwen can ingest roughly 8x more content in a single prompt. For applications that process long documents, entire codebases, or extended conversation histories, that difference eliminates the need for chunking, retrieval, or summarization pipelines. It also means Qwen can maintain coherence across longer interactions without losing context. The multimodal capability - accepting text, images, and video - adds further flexibility that DeepSeek's text-only API does not match.

The pricing simplicity is the other key differentiator. $0.10 in, $0.40 out, every request, regardless of cache state. You can model your costs in a spreadsheet with two numbers and a volume estimate. There are no conditional rates, no cache hit probabilities to estimate, and no surprises when your workload pattern changes. For financial planning and budget approvals, that predictability has real value.

DeepSeek V3.2: Raw Power With Cache Economics

DeepSeek V3.2 is a 671-billion-parameter MoE model activating 37 billion parameters per token - roughly 12x the active parameters of Qwen3.5-Flash. That scale difference shows in the benchmarks. MMLU-Pro at 85.0 is comparable to Qwen, but GPQA Diamond at 82.4 is close, and the coding benchmarks pull ahead: Codeforces at 2386 (versus Qwen's 2028) and SWE-bench Verified at 73.1 (versus 69.2). On AIME 2025, DeepSeek scores 93.1. These are frontier-adjacent numbers from a model available under the MIT license. For our in-depth analysis of DeepSeek V3.2's practical performance, see the full review.

The pricing structure is where things get interesting - and complicated. DeepSeek uses automatic KV cache on disk. When your request shares a prefix with a recent request (same system prompt, same conversation history up to the latest turn), the cached portion of the input is billed at $0.028 per million tokens. That is 72% cheaper than Qwen's flat $0.10 and arguably the cheapest input pricing of any frontier-class model available today. The catch: if you miss the cache - unique prompts, changed system instructions, cold starts - you pay $0.28 per million input tokens. That is 2.8x more expensive than Qwen.

This creates a bimodal cost structure. For chatbot workloads where every request starts with the same system prompt and appends user turns, cache hit rates can exceed 80-90%, and your effective input cost approaches $0.03-0.04 per million tokens. For batch processing workloads with diverse, independent documents, cache hits may be close to zero, and your effective input cost is $0.28 per million tokens - nearly 3x Qwen's price. Your workload pattern determines whether DeepSeek is the cheapest or most expensive option in this comparison.

DeepSeek's architectural innovation - DeepSeek Sparse Attention (DSA) - enables efficient processing within its 128K window, with demonstrations showing it can scale to much longer sequences. But the production API caps at 128K today, and for applications that need more context, that is a hard limit. There is no native multimodal support either - DeepSeek V3.2 processes text only, requiring separate vision or audio pipelines for mixed-media workloads.

Benchmark Comparison

The benchmark picture is surprisingly close on knowledge tasks. MMLU-Pro is a statistical tie at 85.3 versus 85.0. GPQA Diamond gives Qwen a modest 1.8-point edge. The real separation is in coding and math. DeepSeek's Codeforces rating of 2386 versus 2028 means it is performing at the Grandmaster tier in competitive programming. SWE-bench Verified at 73.1 versus 69.2 shows a meaningful gap in real-world software engineering capability. AIME at 93.1 versus Qwen's 89.0 demonstrates stronger mathematical reasoning.

The counterweight is everything else. Qwen has vision capabilities, 8x more context, and multimodal input. DeepSeek processes text only. For applications that need to understand images, process videos, or work with mixed-media content, DeepSeek is simply not an option without bolting on separate vision models.

Both models are fully open-weight under permissive licenses - Qwen under Apache 2.0 and DeepSeek under MIT. Both can be self-hosted. But DeepSeek at 671B total parameters requires substantially more hardware than Qwen at 35B. If self-hosting is part of your strategy, Qwen's smaller footprint is a significant advantage. For more context on navigating open versus proprietary options, see our open-source vs proprietary AI guide.

Pricing Analysis

This is where the comparison requires actual math, because DeepSeek's cache-dependent pricing means your effective cost depends on your workload.

The math tells a clear story. DeepSeek wins on price only when your cache hit rate exceeds roughly 65-75%, depending on your input-to-output ratio. Below that threshold, Qwen's flat pricing is cheaper. Above it, DeepSeek's cache hits make it the cheapest frontier-class API by a wide margin.

Workloads that favor DeepSeek's pricing: conversational agents with fixed system prompts (cache hit rates of 80-95%), agentic loops that repeatedly send similar context, batch processing of queries against the same knowledge base. Workloads that favor Qwen's pricing: diverse document processing, cold-start queries, user-generated content with no shared prefixes, and any application where cache hit rates are unpredictable.

The output pricing is nearly identical ($0.40 vs $0.42), so output-heavy workloads show minimal cost difference regardless of cache behavior.

One more cost factor: Qwen's 1M context eliminates the need for retrieval infrastructure in many cases. If using DeepSeek's 128K window forces you to build a RAG pipeline with vector databases and embedding models, the infrastructure cost of that pipeline may exceed the token savings. The cheapest architecture is often the one with fewer components.

Pros and Cons

Qwen3.5-Flash

Pros:

• Flat, predictable pricing ($0.10/$0.40) - no cache-hit gambling
• 1M token context window - 8x larger than DeepSeek's 128K
• Multimodal input (text, image, video) versus DeepSeek's text-only
• Much smaller model footprint (35B vs 671B) for self-hosting
• Slightly better GPQA Diamond (84.2 vs 82.4)
• Native tool calling and toggleable thinking mode in the API
• Vision benchmarks (MMMU 81.4) with no additional pipeline needed

Cons:

• Lower competitive programming capability (Codeforces 2028 vs 2386)
• Weaker math reasoning (AIME 89.0 vs 93.1)
• Lower SWE-bench Verified (69.2 vs 73.1)
• Less mature developer community compared to DeepSeek's rapid growth
• More expensive than DeepSeek on high-cache-hit workloads
• Alibaba Cloud infrastructure perception in Western markets

DeepSeek V3.2

Pros:

• Cheapest input pricing available at $0.028/M on cache hits
• Stronger coding benchmarks (Codeforces 2386, SWE-bench 73.1)
• Better math reasoning (AIME 93.1)
• Full 671B model available under MIT license
• Automatic KV cache on disk - no manual cache management required
• Strong competitive programming capability at Grandmaster tier
• DeepSeek Sparse Attention for efficient long-sequence processing

Cons:

• Cache miss pricing ($0.28/M input) is 2.8x more expensive than Qwen
• 128K context window limits long-document applications
• Text-only input - no native vision or video processing
• 671B total parameters makes self-hosting hardware-intensive
• Cache-dependent pricing creates unpredictable cost modeling
• No native tool calling comparable to Qwen's built-in support
• Service reliability has been inconsistent during high-demand periods

Verdict

This comparison boils down to two questions: how long are your contexts, and how predictable are your prompts?

Choose Qwen3.5-Flash if you process long documents, need multimodal input, or want pricing you can predict without knowing your cache statistics. The 1M context window is not a luxury - it is a structural advantage that eliminates infrastructure complexity. If your prompts are diverse, user-generated, or frequently changing, Qwen's flat $0.10 input rate will consistently beat DeepSeek's $0.28 cache-miss rate. Also choose Qwen if self-hosting matters: a 35B model is dramatically easier to run than a 671B model. For more on Qwen's broader model family, see our Qwen 3 review.

Choose DeepSeek V3.2 if you are building chatbots, agentic systems, or any application with repeated system prompts where cache hits will be consistently high. At $0.028 per million input tokens on cache hits, nothing else comes close. The coding and math benchmark advantages are real - Codeforces 2386 and AIME 93.1 represent meaningfully stronger capability for technical workloads. If your application is text-only and your prompts are structured for caching, DeepSeek is the better engineering choice. Our DeepSeek V3.2 review covers practical usage in detail.

Choose either if you are primarily concerned with general knowledge tasks where both models perform nearly identically (MMLU-Pro 85.3 vs 85.0). At that level of parity, the decision should be driven by your specific cost model and infrastructure requirements rather than benchmark points. Both are open-weight models under permissive licenses, both deliver frontier-adjacent quality, and both represent the new baseline for what a budget API should offer. The models costing 10x more should be nervous.

For ongoing tracking of how these models compare across the full benchmark suite, check our coding benchmarks leaderboard and open-source LLM leaderboard.

Sources:

• Qwen3.5-35B-A3B Model Card - Hugging Face
• Qwen3.5 Features, Access, and Benchmarks - DataCamp
• Qwen 3.5 Medium Series - MarkTechPost
• DeepSeek V3.2 Release Notes
• DeepSeek API Pricing
• DeepSeek Context Caching Documentation
• DeepSeek V3.2 Performance Analysis - Artificial Analysis
• DeepSeek V3.2 Matches GPT-5 at 10x Lower Cost - Introl
• DeepSeek V3.2 Technical Report - arXiv
• Alibaba Cloud Model Pricing
• Qwen 3.5 Benchmarks and Pricing Guide - Digital Applied
• LLM Benchmarks 2026 - LLM Stats