Model comparison
Phi-4 vs Qwen3.5 122B-A10B
Qwen3.5 122B-A10B is the stronger model overall, scoring 41.6 to 30.9 on the Noometry Index. Phi-4 costs 13× less per token, which makes it the better buy when Qwen3.5 122B-A10B's lead doesn't matter for your workload.
Last verified . 18 shared benchmarks.
Microsoft
Alibaba (Qwen)Summary
- They share 18 benchmarks with published results for both. Phi-4 scores higher in 0 categories and Qwen3.5 122B-A10B in 8 categories; 8 gaps are clear of the uncertainty.
- The widest gap is in writing & preference, where Qwen3.5 122B-A10B leads 60.2 to 40.7.
- The biggest single-benchmark swing is Vectara Hallucination Rate: 3.7% for Phi-4 and 11.2% for Qwen3.5 122B-A10B.
- Phi-4 is cheaper at $0.07 / $0.14 per million input/output tokens, against $0.40 / $3.20 for Qwen3.5 122B-A10B.
- Qwen3.5 122B-A10B accepts more context: 262K tokens versus 128K.
Side by side
| Phi-4 | Qwen3.5 122B-A10B | |
|---|---|---|
| Provider | Microsoft | Alibaba (Qwen) |
| Noometry Index | 30.9 | 41.6 |
| Released | 2024-12-11 | 2026-02-23 |
| Weights | Open | Open |
| Context window | 128K | 262K |
| Max output | 4K | 66K |
| Input $ / M tokens | $0.07 | $0.40 |
| Output $ / M tokens | $0.14 | $3.20 |
| Results tracked | 37 | 27 |
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Category by category
Coding Qwen3.5 122B-A10B leads
Phi-4: 33.8 (#237), Qwen3.5 122B-A10B: 38.8 (#155)
| Benchmark | Phi-4 | Qwen3.5 122B-A10B |
|---|---|---|
| LMArena Coding | 1231 | 1436 |
| LMArena WebDev | — | 1360 |
| SciCode | — | 35.6% |
| BigCodeBench Instruct | 45.5% | — |
| LiveBench Coding | 30.7% | — |
| BigCodeBench Complete | 55.4% | — |
Agentic & Tool Use Not comparable
Phi-4: 22.6 (#120), Qwen3.5 122B-A10B: —
| Benchmark | Phi-4 | Qwen3.5 122B-A10B |
|---|---|---|
| Berkeley Function Calling Leaderboard | 28.8% | — |
| BALROG | 11.6% | — |
Reasoning Qwen3.5 122B-A10B leads
Phi-4: 17.0 (#287), Qwen3.5 122B-A10B: 26.4 (#121)
| Benchmark | Phi-4 | Qwen3.5 122B-A10B |
|---|---|---|
| LMArena Hard Prompts | 1220 | 1421 |
| NYT Connections (extended) | — | 51.7% |
| CritPt | — | 0.9% |
| Chess Puzzles | 1% | — |
| Thematic Generalization | — | 51.2% |
| LiveBench Reasoning | 47.8% | — |
| Mystery Game Puzzles | — | 17% |
| DTBench | — | 84.3% |
| LiveBench Data Analysis | 45.2% | — |
| LMCA | — | 32.2% |
| Epoch Capabilities Index | 130.43 | — |
| LiveBench | 41.6% | — |
Math Qwen3.5 122B-A10B leads
Phi-4: 19.6 (#279), Qwen3.5 122B-A10B: 36.4 (#114)
| Benchmark | Phi-4 | Qwen3.5 122B-A10B |
|---|---|---|
| LMArena Math | 1246 | 1432 |
| OTIS Mock AIME 2024-2025 | 13.8% | — |
| LiveBench Math | 42% | — |
| MATH Level 5 | 64.9% | — |
Knowledge Qwen3.5 122B-A10B leads
Phi-4: 32.5 (#204), Qwen3.5 122B-A10B: 38.2 (#138)
| Benchmark | Phi-4 | Qwen3.5 122B-A10B |
|---|---|---|
| Vectara Hallucination Rate | 3.7% | 11.2% |
| LMArena Expert | 1203 | 1431 |
| GPQA Diamond | 56.1% | — |
| Confabulations | 29.4% | — |
| MMLU | 84.8% | — |
Multimodal Not comparable
Phi-4: —, Qwen3.5 122B-A10B: 39.0 (#52)
| Benchmark | Phi-4 | Qwen3.5 122B-A10B |
|---|---|---|
| LMArena Vision | — | 1245 |
Multilingual Qwen3.5 122B-A10B leads
Phi-4: 37.4 (#232), Qwen3.5 122B-A10B: 51.8 (#101)
| Benchmark | Phi-4 | Qwen3.5 122B-A10B |
|---|---|---|
| LMArena Non-English | 1197 | 1400 |
| LMArena Chinese | 1211 | 1462 |
| LMArena French | 1223 | 1442 |
| LMArena German | 1222 | 1425 |
| LMArena Japanese | 1158 | 1364 |
| LMArena Korean | 1151 | 1353 |
| LMArena Russian | 1209 | 1400 |
| LMArena Spanish | 1234 | 1423 |
Instruction Following Qwen3.5 122B-A10B leads
Phi-4: 60.6 (#246), Qwen3.5 122B-A10B: 74.0 (#109)
| Benchmark | Phi-4 | Qwen3.5 122B-A10B |
|---|---|---|
| LMArena Instruction Following | 1201 | 1399 |
| LiveBench Instruction Following | 58.4% | — |
Long Context Qwen3.5 122B-A10B leads
Phi-4: 36.9 (#221), Qwen3.5 122B-A10B: 43.0 (#105)
| Benchmark | Phi-4 | Qwen3.5 122B-A10B |
|---|---|---|
| LMArena Longer Query | 1217 | 1410 |
Writing & Preference Qwen3.5 122B-A10B leads
Phi-4: 40.7 (#239), Qwen3.5 122B-A10B: 60.2 (#98)
| Benchmark | Phi-4 | Qwen3.5 122B-A10B |
|---|---|---|
| LMArena Text | 1217 | 1417 |
| LMArena Creative Writing | 1182 | 1369 |
| LMArena Multi-Turn | 1206 | 1416 |
| Short-Story Creative Writing | 62.6% | — |
| LiveBench Language | 25.6% | — |
Frequently asked questions
Is Phi-4 better than Qwen3.5 122B-A10B?
Qwen3.5 122B-A10B is the stronger model overall, scoring 41.6 to 30.9 on the Noometry Index. Phi-4 costs 13× less per token, which makes it the better buy when Qwen3.5 122B-A10B's lead doesn't matter for your workload.
Which is cheaper, Phi-4 or Qwen3.5 122B-A10B?
Phi-4 is cheaper. It lists at $0.07 per million input tokens and $0.14 per million output tokens; Qwen3.5 122B-A10B lists at $0.40 and $3.20.
Is Phi-4 or Qwen3.5 122B-A10B better for coding?
Qwen3.5 122B-A10B scores higher on coding benchmarks: 38.8 versus 33.8 in the Noometry coding category.
Which has the bigger context window?
Qwen3.5 122B-A10B does, with 262K tokens against 128K.
How many benchmarks do Phi-4 and Qwen3.5 122B-A10B share?
18 benchmarks have published results for both models. Phi-4 has 37 scored results on Noometry and Qwen3.5 122B-A10B has 27.