Microsoft (3 models) vs Qwen (Alibaba) (52 models) - compared across composite scores, pricing, capabilities, and context windows.
| Microsoft | Score | vs | Qwen (Alibaba) | Score |
|---|---|---|---|---|
| Phi 4 | 60 | Qwen3 8B | 61 | |
| Phi 4 Mini Instruct | 53 | Qwen3 235B A22B | 54 | |
| WizardLM-2 8x22B | 28 | Qwen2.5 Coder 32B Instruct | 35 |
| Capability | Microsoft | Qwen (Alibaba) | Leader |
|---|---|---|---|
Vision | 0/3 | 22/52 | Qwen (Alibaba) |
Reasoning | 0/3 | 27/52 | Qwen (Alibaba) |
Function Calling | 0/3 | 49/52 | Qwen (Alibaba) |
JSON Mode | 2/3 | 50/52 | Qwen (Alibaba) |
Web Search | 0/3 | 0/52 | Tie |
Streaming | 3/3 | 52/52 | Qwen (Alibaba) |
Image Output | 0/3 | 0/52 | Tie |
| Metric | Microsoft | Qwen (Alibaba) |
|---|---|---|
| Cheapest Input (per 1M tokens) | $0.065 Phi 4 | $0.033 Qwen3 235B A22B Instruct 2507 |
| Cheapest Output (per 1M tokens) | $0.140 | $0.100 |
| Most Expensive Input (per 1M tokens) | $0.620 WizardLM-2 8x22B | $1.04 Qwen3.6 Max Preview |
| Most Expensive Output (per 1M tokens) | $0.620 | $6.24 |
| Free Models | 0 | 2 |
| Max Context Window | 128K | 1.0M |
| Model | Score | Input $/M | Output $/M |
|---|---|---|---|
| Phi 4 | 60 | $0.065 | $0.140 |
| Phi 4 Mini Instruct | 53 | $0.080 | $0.350 |
| WizardLM-2 8x22B | 28 | $0.620 | $0.620 |
| Model | Score | Input $/M | Output $/M |
|---|---|---|---|
| Qwen3.5 397B A17B | 80 | $0.390 | $2.34 |
| Qwen3.5-122B-A10B | 78 | $0.260 | $2.08 |
| Qwen3.5-27B | 77 | $0.195 | $1.56 |
| Qwen3.5-35B-A3B | 76 | $0.140 | $1.00 |
| Qwen3.6 Plus | 75 | $0.325 | $1.95 |
| Qwen3.6 Max Preview | 75 | $1.04 | $6.24 |
| Qwen3 VL 235B A22B Instruct | 69 | $0.200 | $0.880 |
| Qwen3.5-Flash | 69 | $0.065 | $0.260 |
| Qwen3 Max Thinking | 68 | $0.780 | $3.90 |
| Qwen3 VL 235B A22B Thinking | 68 | $0.260 | $2.60 |
| Qwen3 Max | 67 | $0.780 | $3.90 |
| Qwen3 Next 80B A3B Instruct (free) | 67 | Free | Free |
| Qwen3 Next 80B A3B Instruct | 67 | $0.090 | $1.10 |
| Qwen3.5-9B | 67 | $0.040 | $0.150 |
| Qwen3 235B A22B Thinking 2507 | 65 | $0.150 | $1.50 |
| Qwen3 235B A22B Instruct 2507 | 65 | $0.071 | $0.100 |
| Qwen3 30B A3B Thinking 2507 | 64 | $0.080 | $0.400 |
| Qwen3 Next 80B A3B Thinking | 64 | $0.098 | $0.780 |
| Qwen3 30B A3B | 64 | $0.090 | $0.450 |
| Qwen3 8B | 61 | $0.050 | $0.400 |
Compare any two AI providers side-by-side.
Microsoft's lean portfolio focuses on general-purpose models like Phi 4 (32/100 score) at competitive prices ($0.140-$0.620/M tokens), reflecting a strategy of deep integration with existing enterprise tools rather than model diversity. In contrast, Qwen's 50-model portfolio with 36 open source variants and specialized capabilities (45/50 with function calling, 24/50 with reasoning) targets developers who need specific tools for specific tasks, accepting higher complexity for greater flexibility.
The gap represents nearly double the performance, with Qwen3.5-Flash at 60/100 competing with mid-tier commercial models while Microsoft's best offering scores in the lower third of benchmarks. This translates to practical differences in complex tasks: Qwen's models handle 1M token contexts versus Microsoft's 66K maximum, and 19 of Qwen's 50 models support vision tasks compared to 0 from Microsoft.
Qwen's pricing reflects a tiered strategy with 2 free models for experimentation, budget options at $0.090/M competing with open source deployments, and premium models up to $4.16/M for specialized enterprise workloads. Microsoft's narrower $0.140-$0.620/M range targets predictable enterprise budgets but offers no free tier, effectively ceding the hobbyist and research markets to competitors.
Microsoft's complete absence of function calling eliminates agent workflows, API integrations, and tool-augmented applications that form the backbone of modern AI systems. Qwen's 45 function-calling models enable everything from automated customer service (using their 19 vision-capable models for screenshot analysis) to complex multi-step reasoning chains (leveraging their 24 reasoning-optimized variants), while Microsoft users must implement these capabilities through external orchestration.
Microsoft's value proposition isn't in raw model performance (29/100 average vs Qwen's 45/100) but in enterprise integration: Azure infrastructure, compliance certifications, and unified billing with existing Microsoft services. The 2 open source Microsoft models also avoid vendor lock-in concerns despite lower scores, while Qwen's 36 open source models require navigating Alibaba's ecosystem and potential geopolitical considerations for Western enterprises.
Despite Qwen's massive 50-model portfolio and Microsoft's enterprise focus, neither provider offers native web search integration, highlighting that real-time information access remains dominated by specialized providers like Perplexity. This gap forces developers to build custom RAG pipelines or accept static knowledge cutoffs, particularly limiting given Qwen's otherwise comprehensive capability coverage (vision in 38% of models, reasoning in 48%, function calling in 90%).