Meta (Llama) vs Qwen (Alibaba)
Meta (Llama) (8 models) vs Qwen (Alibaba) (49 models) - compared across composite scores, pricing, capabilities, and context windows.
Meta (Llama)
View all modelsQwen (Alibaba)
View all modelsHead-to-Head: Meta (Llama) vs Qwen (Alibaba) Model Matchups
Capability Comparison
| Capability | Meta (Llama) | Qwen (Alibaba) | Leader |
|---|---|---|---|
Vision | 3/8 | 23/49 | Qwen (Alibaba) |
Reasoning | 0/8 | 30/49 | Qwen (Alibaba) |
Function Calling | 5/8 | 47/49 | Qwen (Alibaba) |
JSON Mode | 7/8 | 48/49 | Qwen (Alibaba) |
Web Search | 0/8 | 0/49 | Tie |
Streaming | 8/8 | 49/49 | Qwen (Alibaba) |
Image Output | 0/8 | 0/49 | Tie |
Pricing Comparison
| Metric | Meta (Llama) | Qwen (Alibaba) |
|---|---|---|
| Cheapest Input (per 1M tokens) | $0.027 Llama 3.1 8B Instruct | $0.030 Qwen3.7 Flash |
| Cheapest Output (per 1M tokens) | $0.080 | $0.130 |
| Most Expensive Input (per 1M tokens) | $0.400 Llama 4 Maverick | $2.00 Qwen3.6 Max Preview |
| Most Expensive Output (per 1M tokens) | $0.800 | $6.16 |
| Free Models | 0 | 0 |
| Max Context Window | 1.3M | 1.0M |
All Meta (Llama) Models (8)
| Model | Score | Input $/M | Output $/M |
|---|---|---|---|
| Llama 4 Maverick | 68 | $0.200 | $0.800 |
| Llama 3.3 70B Instruct | 67 | $0.100 | $0.320 |
| Llama 3.1 70B Instruct | 65 | $0.400 | $0.400 |
| Llama 4 Scout | 55 | $0.100 | $0.300 |
| Llama 3.1 8B Instruct | 45 | $0.050 | $0.080 |
| Llama Guard 4 12B | 40 | $0.180 | $0.180 |
| Llama 3.2 3B Instruct | 34 | $0.050 | $0.330 |
| Llama 3.2 1B Instruct | 18 | $0.027 | $0.201 |
All Qwen (Alibaba) Models (49)
| Model | Score | Input $/M | Output $/M |
|---|---|---|---|
| Qwen3.8 Max | 81 | $2.00 | $6.00 |
| Qwen3.5 397B A17B | 79 | $0.390 | $2.34 |
| Qwen3.5-122B-A10B | 78 | $0.290 | $2.40 |
| Qwen3.5-27B | 77 | $0.195 | $1.56 |
| Qwen3.5-35B-A3B | 76 | $0.140 | $1.00 |
| Qwen3.7 Plus | 76 | $0.320 | $1.28 |
| Qwen3.7 Max | 75 | $1.48 | $4.42 |
| Qwen3.5 Plus 2026-04-20 | 75 | $0.300 | $1.80 |
| Qwen3.6 Max Preview | 75 | $1.03 | $6.16 |
| Qwen3.6 Plus | 74 | $0.325 | $1.95 |
| Qwen3 VL 235B A22B Thinking | 69 | $0.400 | $4.00 |
| Qwen3 VL 235B A22B Instruct | 69 | $0.210 | $1.90 |
| Qwen3.5-Flash | 69 | $0.065 | $0.260 |
| Qwen3.5 Plus 2026-02-15 | 68 | $0.260 | $1.56 |
| Qwen3 Max Thinking | 68 | $0.780 | $3.90 |
| Qwen3 Max | 67 | $0.780 | $3.90 |
| Qwen3 Next 80B A3B Thinking | 67 | $0.150 | $1.20 |
| Qwen3 Next 80B A3B Instruct | 67 | $0.090 | $1.10 |
| Qwen3.5-9B | 67 | $0.100 | $0.150 |
| Qwen3 235B A22B Thinking 2507 | 66 | $0.230 | $2.30 |
More Provider Comparisons
Compare any two AI providers side-by-side.
Qwen pursues a breadth-first strategy with 36 open source models covering diverse capabilities - 45 out of 50 models support function calling and 24 support reasoning. Meta's concentrated approach delivers only 14 models total, with zero reasoning support and just 7 models with function calling, suggesting they prioritize foundational research over comprehensive coverage.
Meta's lowest tier at $0.040/M tokens beats Qwen's $0.090/M entry point by 55%, but this advantage diminishes at scale - Qwen's top model (Qwen3.5-Flash at 60/100) outscores Meta's best (Llama 4 Maverick at 54/100) while Qwen's $4.16/M premium tier suggests enterprise-grade features. Both providers offer 2 free models, making initial evaluation risk-free.
While both max out at 1.0M context, Qwen leverages this with 19 vision-capable models (38% of portfolio) versus Meta's 4 (28%), and critically adds reasoning to 48% of its lineup while Meta has zero reasoning models. This suggests Qwen optimizes for multimodal and complex cognitive tasks while Meta focuses on pure text generation efficiency.
Qwen dominates function calling with 45 out of 50 models (90%) supporting it versus Meta's 7 out of 14 (50%), but Meta's 100% open source commitment means all 7 are freely modifiable. Qwen's 36 open source models include most function-calling variants, offering 5x more open source function-calling options than Meta's entire portfolio.
Meta offers 4 vision models (all open source) starting at $0.040/M tokens, while Qwen provides 19 vision options with only partial open source coverage at $0.090/M minimum. For pure cost optimization with vision needs, Meta's smaller but fully open portfolio saves 55% on inference costs, though Qwen's 4.75x larger vision selection provides more architectural options for specific use cases.