Phi 4 vs Qwen2.5 Coder 32B Instruct
Qwen2.5 Coder 32B Instruct leads Phi 4 on the Artificial Analysis Intelligence Index, 6.7 to 5.9, and Phi 4 costs 7.1x less per output token ($0.14 vs $1.00 per million). Updated October 21, 2026; 29 benchmarks have a result for both.
Phi 4 or Qwen2.5 Coder 32B Instruct: which to pick
Neither model has a coding score yet, so the two cannot be ranked on coding.
Pick Phi 4 for output tokens 7.1x cheaper ($0.14 vs $1.00 per million) and work measured by LiveCodeBench (Artificial Analysis run) or LMArena Text: coding, where it leads.
- Shared results
- 29
- Phi 4 only
- 17
- Qwen2.5 Coder 32B Instruct only
- 6
- Like-for-like categories
- 6 of 9
Only benchmarks that tested both models count toward a pick; a result only one model has is shown but not compared.
Which one for your work
Each card is decided by one number both models carry. Where only one has it, the card says so and makes no call.
Coding
Vibe Coding Index, six public coding benchmarks
No call
Neither has a coding score yet.
One side not measured
Intelligence
AA Intelligence Index
Qwen2.5 Coder 32B Instruct
Qwen2.5 Coder 32B Instruct leads, 6.7 against 5.9.
Same board, both measured
Long documents
Listed context window
Qwen2.5 Coder 32B Instruct
Qwen2.5 Coder 32B Instruct takes 33K tokens against 16K.
Listed by OpenRouter
Everyday cost
1K input + 500 output tokens
Phi 4
Phi 4 $0.0001, Qwen2.5 Coder 32B Instruct $0.0012 per turn.
List prices
At a glance
- Coding index
- Phi 4 not benchmarked, Qwen2.5 Coder 32B Instruct not benchmarked.
- Widest category
- Intelligence, 7.3 points apart on average over 2 shared benchmarks.
- Widest single gap
- GPQA Diamond (Artificial Analysis run): Phi 4 57.5% against Qwen2.5 Coder 32B Instruct 41.7%, 15.8 points apart on a 0 to 100 scale.
- Output price
- Phi 4 $0.14 against Qwen2.5 Coder 32B Instruct $1.00 per million output tokens, 7.1x apart.
- Context window
- Phi 4 takes 16K tokens, Qwen2.5 Coder 32B Instruct 33K.
Benchmark by benchmark
First the score: boards each model leads, per category, where both have a result. Then every board, a category to a fold; bars are each result on a 0 to 100 scale, so compare the two in a row, not one row with another.
| Category | Phi 4 | Bars | Qwen2.5 Coder 32B Instruct | Ties |
|---|---|---|---|---|
| Coding | 0 | 3 | ||
| Intelligence | 1 | 1 | ||
| Reasoning | 2 | 3 | ||
| Math | 1 | 1 | ||
| Knowledge | 3 | 3 | ||
| General | 3 | 8 | ||
| All shared boards | 10 | 19 |
CodingLike-for-likeSix of these boards feed the Vibe Coding Index03
| Benchmark | Phi 4 | Qwen2.5 Coder 32B Instruct | Leader |
|---|---|---|---|
| Aider polyglot | Not measured | 16.4% | Only Qwen2.5 Coder 32B Instruct measured |
| SWE-bench Verified | Not measured | 47.0% | Only Qwen2.5 Coder 32B Instruct measured |
| LiveCodeBench (Artificial Analysis run) | 23.1% | 29.5% | Qwen2.5 Coder 32B Instruct |
| Terminal-Bench Hard (Artificial Analysis run) | 3.8% | Not measured | Only Phi 4 measured |
| LMArena Text: coding | 1231 | 1276 | Qwen2.5 Coder 32B Instruct |
| LMArena Text: software and IT | 1228 | 1261 | Qwen2.5 Coder 32B Instruct |
| Neither model has a result on the other 33 boards here | |||
IntelligenceLike-for-likePublic boards, shown as published, kept out of the index11
| Benchmark | Phi 4 | Qwen2.5 Coder 32B Instruct | Leader |
|---|---|---|---|
| AA Intelligence Index | 5.9 | 6.7 | Qwen2.5 Coder 32B Instruct |
| Epoch Capabilities Index | 130.5 | 119.5 | Phi 4 |
Agentic workDirectional onlyPublic boards, shown as published, kept out of the index3 boards, none shared
| Benchmark | Phi 4 | Qwen2.5 Coder 32B Instruct | Leader |
|---|---|---|---|
| τ²-Bench Telecom | 0.0% | Not measured | Only Phi 4 measured |
| BALROG | 11.6% | Not measured | Only Phi 4 measured |
| Berkeley Function Calling Leaderboard (BFCL) | 28.8% | Not measured | Only Phi 4 measured |
| Neither model has a result on the other 38 boards here | |||
ReasoningLike-for-likePublic boards, shown as published, kept out of the index23
| Benchmark | Phi 4 | Qwen2.5 Coder 32B Instruct | Leader |
|---|---|---|---|
| GPQA Diamond (Artificial Analysis run) | 57.5% | 41.7% | Phi 4 |
| Humanity's Last Exam (Artificial Analysis run) | 3.8% | 3.5% | Phi 4 |
| GPQA Diamond (Epoch AI run) | 56.1% | Not measured | Only Phi 4 measured |
| LMArena Text: hard prompts | 1220 | 1251 | Qwen2.5 Coder 32B Instruct |
| LMArena Text: expert | 1203 | 1221 | Qwen2.5 Coder 32B Instruct |
| LMArena Text: hard prompts in English | 1230 | 1269 | Qwen2.5 Coder 32B Instruct |
| Neither model has a result on the other 16 boards here | |||
MathLike-for-likePublic boards, shown as published, kept out of the index11
| Benchmark | Phi 4 | Qwen2.5 Coder 32B Instruct | Leader |
|---|---|---|---|
| AA Math Index | 18.0 | Not measured | Only Phi 4 measured |
| AIME 2025 (Artificial Analysis run) | 18.0% | Not measured | Only Phi 4 measured |
| OTIS Mock AIME | 13.8% | Not measured | Only Phi 4 measured |
| MATH level 5 | 64.9% | Not measured | Only Phi 4 measured |
| GSM8K | Not measured | 93.0% | Only Qwen2.5 Coder 32B Instruct measured |
| LMArena Text: math | 1246 | 1251 | Qwen2.5 Coder 32B Instruct |
| LMArena Text: mathematical work | 1254 | 1243 | Phi 4 |
| Neither model has a result on the other 6 boards here | |||
KnowledgeLike-for-likePublic boards, shown as published, kept out of the index33
| Benchmark | Phi 4 | Qwen2.5 Coder 32B Instruct | Leader |
|---|---|---|---|
| MMLU Pro (Artificial Analysis run) | 71.4% | 63.5% | Phi 4 |
| MMLU | 84.8% | 79.1% | Phi 4 |
| AI2 Reasoning Challenge | Not measured | 70.5% | Only Qwen2.5 Coder 32B Instruct measured |
| LMArena Text: science | 1206 | 1221 | Qwen2.5 Coder 32B Instruct |
| LMArena Text: medicine and healthcare | 1202 | 1187 | Phi 4 |
| LMArena Text: legal and government | 1241 | 1257 | Qwen2.5 Coder 32B Instruct |
| LMArena Text: business and finance | 1199 | 1224 | Qwen2.5 Coder 32B Instruct |
| Neither model has a result on the other 11 boards here | |||
Web designNot comparablePublic boards, shown as published, kept out of the index0 boards, none shared
| Benchmark | Phi 4 | Qwen2.5 Coder 32B Instruct | Leader |
|---|---|---|---|
| Neither model has a result on 9 boards here | |||
GeneralLike-for-likePublic boards, shown as published, kept out of the index38
| Benchmark | Phi 4 | Qwen2.5 Coder 32B Instruct | Leader |
|---|---|---|---|
| IFBench | 23.5% | Not measured | Only Phi 4 measured |
| Long context reasoning (AA-LCR) | 0.0% | Not measured | Only Phi 4 measured |
| Creative writing (Lech Mazur) | 62.6% | Not measured | Only Phi 4 measured |
| HellaSwag | Not measured | 83.0% | Only Qwen2.5 Coder 32B Instruct measured |
| WinoGrande | Not measured | 80.8% | Only Qwen2.5 Coder 32B Instruct measured |
| LMArena Text | 1217 | 1230 | Qwen2.5 Coder 32B Instruct |
| LMArena Text: instruction following | 1201 | 1223 | Qwen2.5 Coder 32B Instruct |
| LMArena Text: creative writing | 1181 | 1174 | Phi 4 |
| LMArena Text: English | 1231 | 1248 | Qwen2.5 Coder 32B Instruct |
| LMArena Text: non-English | 1197 | 1205 | Qwen2.5 Coder 32B Instruct |
| LMArena Text: Chinese | 1212 | 1222 | Qwen2.5 Coder 32B Instruct |
| LMArena Text: German | 1222 | Not measured | Only Phi 4 measured |
| LMArena Text: Japanese | 1158 | Not measured | Only Phi 4 measured |
| LMArena Text: Korean | 1151 | Not measured | Only Phi 4 measured |
| LMArena Text: French | 1224 | Not measured | Only Phi 4 measured |
| LMArena Text: Spanish | 1234 | Not measured | Only Phi 4 measured |
| LMArena Text: Russian | 1209 | 1228 | Qwen2.5 Coder 32B Instruct |
| LMArena Text: multi-turn | 1206 | 1222 | Qwen2.5 Coder 32B Instruct |
| LMArena Text: longer queries | 1217 | 1251 | Qwen2.5 Coder 32B Instruct |
| LMArena Text: writing and language | 1198 | 1191 | Phi 4 |
| LMArena Text: entertainment and media | 1162 | 1150 | Phi 4 |
| Neither model has a result on the other 25 boards here | |||
Image generationNot comparablePublic boards, shown as published, kept out of the index0 boards, none shared
| Benchmark | Phi 4 | Qwen2.5 Coder 32B Instruct | Leader |
|---|---|---|---|
| Neither model has a result on 5 boards here | |||
What each workload costs
Three fixed token mixes at each model's listed price, so per-token rates become a bill.
Chat turn
1K input + 500 output tokens
Phi 4 costs less
Repository review
50K input + 3K output tokens
Phi 4 costs less
Agent loop
200K input + 10K output tokens
Phi 4 costs less
Listed OpenRouter rates, without cache or batch discounts. A workload that does not fit a model's context window would need splitting.
Specification differences
The leading value in a row is marked; a row with a missing value names no leader. Our two rows are marked in red.
| Specification | Microsoft Phi 4 | Qwen2.5 Coder 32B Instruct |
|---|---|---|
| Maker | Microsoft | Qwen |
| Weights | Open weights | Open weights |
| Released | January 10, 2025 | November 11, 2024 |
| Context window | 16K | 33K |
| Input $/Mtok | $0.07 | $0.66 |
| Output $/Mtok | $0.14 | $1.00 |
| Coding index | Not benchmarked | Not benchmarked |
| Independent measurements behind it | 0 | 0 |
| Cost per solved task | Not computable | Not computable |
| Source | OpenRouter listing | OpenRouter listing |
Questions
Is Phi 4 better than Qwen2.5 Coder 32B Instruct for coding?
Neither model has a coding score yet, so the two cannot be ranked on coding. Pick Phi 4 for output tokens 7.1x cheaper ($0.14 vs $1.00 per million) and work measured by LiveCodeBench (Artificial Analysis run) or LMArena Text: coding, where it leads.
Which is cheaper, Phi 4 or Qwen2.5 Coder 32B Instruct?
Phi 4 costs $0.07 per million input tokens and $0.14 per million output tokens; Qwen2.5 Coder 32B Instruct costs $0.66 and $1.00. Cost per solved task is known for neither of them.
Which has the bigger context window, Phi 4 or Qwen2.5 Coder 32B Instruct?
Qwen2.5 Coder 32B Instruct, with 33K tokens against 16K.
How many benchmarks is the Phi 4 vs Qwen2.5 Coder 32B Instruct comparison based on?
29 benchmarks have a result for both models as of October 21, 2026. Phi 4's coding index rests on 0 independent benchmarks and Qwen2.5 Coder 32B Instruct's on 0. Where only one model has a result, the page says so instead of naming a winner.