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Microsoft vs Qwen (Alibaba)

Microsoft (1 models) vs Qwen (Alibaba) (43 models) - compared across composite scores, pricing, capabilities, and context windows.

Models
1
Avg Score
61
Top Model
Score: 61
Price Range
$0.140 - $0.140
per 1M output tokens
1 open source16K max context

Qwen (Alibaba)

View all models
Models
43
Avg Score
44
Top Model
Score: 71
Price Range
$0.130 - $12.00
per 1M output tokens
29 open source1.0M max context

Head-to-Head: Microsoft vs Qwen (Alibaba) Model Matchups

MicrosoftScorevsQwen (Alibaba)Score
Phi 461Qwen3 235B A22B Thinking 250765

Capability Comparison

CapabilityMicrosoftQwen (Alibaba)Leader
Vision
0/126/43Qwen (Alibaba)
Reasoning
0/128/43Qwen (Alibaba)
Function Calling
0/141/43Qwen (Alibaba)
JSON Mode
1/142/43Qwen (Alibaba)
Web Search
0/10/43Tie
Streaming
1/143/43Qwen (Alibaba)
Image Output
0/10/43Tie

Pricing Comparison

MetricMicrosoftQwen (Alibaba)
Cheapest Input (per 1M tokens)$0.070
Phi 4
$0.030
Qwen3.7 Flash
Cheapest Output (per 1M tokens)$0.140$0.130
Most Expensive Input (per 1M tokens)$0.070
Phi 4
$4.00
Qwen3.8 Max Prime
Most Expensive Output (per 1M tokens)$0.140$12.00
Free Models00
Max Context Window16K1.0M

All Microsoft Models (1)

ModelScoreInput $/MOutput $/M
Phi 461$0.070$0.140

All Qwen (Alibaba) Models (43)

ModelScoreInput $/MOutput $/M
Qwen3.5 397B A17B71$0.500$3.60
Qwen3.5-122B-A10B70$0.260$2.08
Qwen3.5-27B69$0.260$2.60
Qwen3.5-35B-A3B69$0.140$1.00
Qwen3.5-9B67$0.170$0.250
Qwen3 235B A22B Thinking 250765$0.450$3.50
Qwen3 235B A22B Instruct 250765$0.140$0.800
Qwen3.8 Max Prime40$4.00$12.00
Qwen3.8 Omni Flash40$0.150$0.470
Qwen3.8 Max (0902)40$2.00$6.00
Qwen3.8 Flash40$0.150$0.470
Qwen3.8 27B40$0.150$1.88
Qwen3.8 27B (batch)40$0.160$2.00
Qwen3.8 2.4T A95B40$2.00$6.00
Qwen3.7 Flash40$0.030$0.130
Qwen3.7 Plus40$0.320$1.28
Qwen3.7 Plus (batch)40$0.230$0.920
Qwen3.7 Max40$1.48$4.42
Qwen3.5 Plus 2026-04-2040$0.300$1.80
Qwen3.6 Flash40$0.188$1.13
Frequently Asked Questions

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%).

Microsoft vs Qwen (Alibaba) - AI Provider Comparison (2026) | LM Market Cap