Qwen3.5-27B vs Phi-4: When Twice the Parameters Is Not Twice as Obvious

On paper, this should not be a fair fight. Qwen3.5-27B has 27 billion parameters. Phi-4 has 14 billion. Qwen has a 262K context window. Phi-4 has 16K. Qwen runs native multimodal with video understanding. Phi-4 is text-only. By every structural metric, Qwen is the bigger, more capable model - and the benchmarks mostly confirm this.

But "mostly" is doing real work in that sentence. Phi-4 scores 84.8 on MMLU versus Qwen's 93.2 on MMLU-Redux - a gap, but not the chasm you would expect from a model with half the parameters. On MATH, Phi-4 posts 80.4 compared to Qwen's HMMT February 2025 score of 92.0. On GPQA, Phi-4's 56.1 is well behind Qwen's 85.5, but it is ahead of many models that are 3-5x its size. Microsoft's synthetic data training approach has produced a model that punches above its weight class on STEM tasks - and at half the VRAM cost, that matters for a lot of deployment scenarios.

The real question is not "which model is better" but "when is the cheaper, faster model good enough?"

Quick Comparison

Qwen3.5-27B: The Full-Spectrum Model

Qwen3.5-27B is the dense flagship in Alibaba's February 2026 release, and it is designed to compete not just with other 27B models but with much larger ones. The benchmark profile is comprehensive: MMLU-Pro 86.1, GPQA Diamond 85.5, SWE-bench Verified 72.4, LiveCodeBench v6 80.7, IFEval 95.0. These are not narrow wins on cherry-picked benchmarks - they represent broad capability across knowledge, reasoning, coding, and instruction following.

The architecture is a 64-layer hybrid that alternates between Gated DeltaNet (linear attention) and Gated Attention (full quadratic) in a 3:1 ratio. This is not a standard transformer. The DeltaNet layers scale linearly with sequence length, which is why Qwen can offer a 262K native context window - and extend past 1 million tokens with YaRN scaling - without the memory explosion that pure transformer models face. For workloads that require processing entire repositories, long technical documents, or extended multi-turn conversations, this architectural advantage is not theoretical. It translates to real deployment cost savings at long context lengths.

The multimodal capabilities add another dimension that Phi-4 simply cannot match. Qwen3.5-27B processes text, images, and video natively through early-fusion training. On MMMU, it scores 82.3 - a vision-language benchmark where the model must reason about images. MathVision at 86.0 and DynaMath at 87.7 show strong mathematical reasoning from visual inputs. If your pipeline involves any form of visual input - screenshots, diagrams, charts, video frames - Qwen is the only option in this comparison.

The broader Qwen 3.5 family provides upgrade and downgrade paths. If 27B is too heavy, the Qwen3.5-35B-A3B MoE model activates only 3B parameters per token while maintaining competitive benchmarks. If you need more power, the Qwen3.5-122B-A10B scales up to 10B active parameters. This ecosystem flexibility does not exist in the Phi family.

Phi-4: The Efficiency Argument

Microsoft released Phi-4 in December 2024 with a thesis that matters: you can achieve strong performance with fewer parameters if you train on the right data. The model's 9.8 trillion training tokens include 400 billion synthetic tokens generated specifically for STEM reasoning - a data-engineering approach that has produced outsized results relative to the parameter count.

The STEM numbers are legitimately impressive for 14B parameters. MMLU at 84.8 puts Phi-4 ahead of Llama-3.3-70B on the same evaluation. MATH at 80.4 beats GPT-4o's 74.6 on SimpleEval. GPQA at 56.1 outperforms GPT-4o-mini (40.9) and Qwen 2.5-14B (42.9) by double-digit margins. On AMC-10/12 math competition problems, Phi-4 scores 91.8%. These are not the benchmarks of a "small" model - they are competitive with models 3-5x larger on STEM-specific tasks.

The practical deployment case for Phi-4 centers on cost. At 14B parameters, the model requires roughly 28GB of VRAM in BF16 - fits on a single RTX 4090 or equivalent. In Q4 quantization, it drops to approximately 8-9GB - within reach of consumer GPUs like the RTX 3080 or even Apple M-series machines with unified memory. That is half the hardware cost of Qwen's 27B at every precision level. For high-throughput deployments where you are running hundreds of requests per second, the infrastructure savings compound.

The 16K context window is the elephant in the room. In a world where competing models offer 128K to 262K tokens, Phi-4's 16K ceiling is a hard constraint. Long documents, multi-file code analysis, extended conversations - any workflow that requires more than about 12,000 words of context simply will not work with Phi-4. Microsoft has extended variants that push to 64K, but the base model's 16K is a non-starter for many modern agentic and RAG workloads. The MIT license is marginally more permissive than Apache 2.0 (though in practice the difference is academic), and Microsoft's broader AI ecosystem - Azure AI, Copilot integration, the Phi model family - provides its own deployment advantages for teams already in that stack.

Detailed Benchmark Comparison

N/R = Not Reported on comparable evaluation. N/A = Not Applicable (model lacks capability).

The benchmark comparison reveals a predictable pattern: Qwen wins on every metric where both models report scores, often by substantial margins. The GPQA gap of 29.4 points is the most significant - this is the benchmark that separates models that can reason through complex scientific problems from those that cannot. Qwen's 85.5 puts it in frontier-model territory. Phi-4's 56.1 is strong for 14B but a different capability tier.

However, the efficiency narrative matters. Phi-4 achieves roughly 65-70% of Qwen's performance on comparable benchmarks at 52% of the parameter count. The MMLU gap is only 8.4 points despite the 2x parameter difference. On MATH, the gap is 11.6 points. These are not proportional to the size difference - Phi-4's training approach delivers disproportionate value per parameter, especially on STEM tasks.

The missing data points are worth noting. Phi-4 was released before SWE-bench Verified and LiveCodeBench became standard evaluation benchmarks, so direct comparison on modern coding metrics is not available. Similarly, Qwen does not report HumanEval in the same format as Phi-4's SimpleEval benchmark, making direct coding comparison difficult. For standardized comparisons, check the Coding Benchmarks Leaderboard and the Open Source LLM Leaderboard.

Pricing and Deployment

The deployment cost difference is the strongest argument for Phi-4. At half the parameter count, Phi-4 requires half the VRAM, generates tokens roughly twice as fast on identical hardware, and fits on consumer GPUs that Qwen cannot touch in full precision. A single RTX 4090 can run Phi-4 in BF16 with room to spare. Running Qwen3.5-27B in BF16 requires an A100 80GB, H100, or a dual-GPU setup.

For API-based deployment, the per-token pricing is surprisingly close. Azure charges $0.13/M input for Phi-4, while Qwen's hosted options run approximately $0.10/M. The output pricing gap is wider - Azure's $0.50/M for Phi-4 output versus Qwen's $0.10/M - which means for output-heavy workloads (code generation, long-form writing), Qwen is actually cheaper per token through its managed API despite being the larger model.

For self-hosting at scale, the infrastructure math is straightforward. If you have a fleet of RTX 4090 machines, Phi-4 is the only option. If you have A100/H100 machines, Qwen gives you dramatically better benchmarks for the same GPU slot. The right answer depends entirely on your hardware and workload - there is no universal "better" here.

Both licenses - Apache 2.0 and MIT - are maximally permissive for commercial use. Neither requires attribution, neither restricts commercial deployment, and neither includes the kind of behavioral-use restrictions found in Meta's Llama license or Google's Gemma terms. This is a non-factor in the comparison.

Pros and Cons

Qwen3.5-27B

Pros:

• MMLU-Pro 86.1, GPQA Diamond 85.5 - frontier-tier reasoning at open-weight scale
• SWE-bench Verified 72.4 - strong automated code repair capability
• 262K context window (1M+ extended) - 16x Phi-4's capacity
• Native multimodal: text, image, and video understanding
• Part of Qwen 3.5 family with MoE and larger dense variants
• Gated DeltaNet architecture - efficient long-context scaling
• Apache 2.0 license

Cons:

• 27B parameters requires 54GB VRAM (BF16) - no consumer GPU deployment
• Newer architecture with less mature framework support
• February 2026 release - limited community testing
• Roughly half the throughput of Phi-4 on identical hardware
• Self-reported benchmarks pending independent verification
• Overkill for simple STEM tasks where Phi-4 is sufficient

Phi-4

Pros:

• MMLU 84.8, MATH 80.4, GPQA 56.1 - strong STEM at 14B parameters
• Half the VRAM cost - fits on RTX 4090 in BF16
• ~2x faster inference than 27B models on identical hardware
• 9.8T tokens including 400B synthetic data - efficient training approach
• MIT license - maximally permissive
• Mature ecosystem with Azure AI integration and Microsoft tooling
• Well-validated - community-tested since December 2024

Cons:

• 16K context window is severely limiting for modern workloads
• No vision or multimodal capabilities - text only
• GPQA 56.1 trails Qwen by 29 points - not viable for hard reasoning tasks
• No SWE-bench or LiveCodeBench scores available
• December 2024 training data cutoff limits current knowledge
• No MoE or family variants - standalone model only
• HumanEval 82.6 is good but not exceptional by 2026 standards

Verdict

This is not a comparison of competing models at the same tier. It is a comparison of two different deployment strategies: maximum capability (Qwen) versus maximum efficiency (Phi-4). The right choice depends on where your workload falls on that spectrum.

Choose Qwen3.5-27B if your tasks require reasoning depth that 14B parameters cannot provide. Graduate-level science (GPQA 85.5), automated code repair (SWE-bench 72.4), long-document analysis (262K context), or multimodal understanding - these are use cases where the 2x parameter investment pays for itself in accuracy gains that the smaller model simply cannot match. If you are building coding agents, research assistants, or complex analytical pipelines, Qwen is the right tool. The 29-point GPQA gap is not something you can compensate for with clever prompting.

Choose Phi-4 if your workload is STEM-focused, fits within 16K tokens, and you are optimizing for cost per query. Phi-4 delivers 65-70% of Qwen's capability at 50% of the infrastructure cost - and for many production workloads, that tradeoff is the right one. Tutoring systems, code completion (not repair), mathematical problem-solving, classification tasks, and any high-throughput text pipeline where individual requests are short and STEM-oriented - these are Phi-4's sweet spot. The RTX 4090 deployment story makes it accessible to teams without enterprise GPU budgets.

Choose either if you are evaluating open-weight models for general-purpose text tasks and want to understand the cost-capability frontier. Running both in parallel - Phi-4 for simple queries and Qwen for complex ones, routed by a lightweight classifier - is an engineering pattern worth considering. The inference cost savings on the easy queries often pay for the capability premium on the hard ones. For more on how these models fit into the broader landscape, see the Open Source LLM Leaderboard and our open source vs proprietary AI guide.

Sources:

• Qwen3.5-27B Model Card (HuggingFace)
• Qwen3.5: Towards Native Multimodal Agents (Blog)
• Phi-4 Model Card (HuggingFace)
• Phi-4 Technical Report (arXiv)
• Phi-4 Technical Report (Microsoft Research)
• Phi-4 Performance Analysis (Artificial Analysis)
• Phi-4 Azure Pricing
• Qwen3.5 Features, Access, and Benchmarks (DataCamp)