Alternatives to Qwen2.5 Coder 32B Instruct
Qwen LMArena 1230 $0.745/M on a balanced workload prices as of 2026-10-06
65 models are higher-scoring or cheaper, with neither dimension worse.
Of the 144 models carrying both an independent score and a published price at the same delivery mode, 81 score at least as high as Qwen2.5 Coder 32B Instruct and cost no more under at least one listed workload, with at least one of those dimensions improved. 65 match or beat its recorded context, maximum output, input modes, tool use and extended reasoning. 16 have a recorded capability loss, named on every row.
Dominance is not a property of a model. It is a property of a model, a capability lens and a workload, and all three are named on every row. Scores are LMArena Elo, used under CC BY 4.0 from the official dataset; prices are blended per million tokens. A higher score is not a drop-in replacement.
This page is built only while a candidate improves score or price without worsening the other under a listed workload. When that comparison no longer holds, it is not generated.
Where it holds
| Workload | Qwen2.5 Coder 32B Instruct $/M | Recorded capabilities preserved | Recorded capability losses |
|---|---|---|---|
| Balanced | $0.745 | 50 | 13 |
| Summarise | $0.677 | 65 | 15 |
| Chat | $0.796 | 45 | 13 |
| Code gen | $0.864 | 40 | 13 |
| Agentic | $0.711 | 63 | 15 |
Workload mixes are defined on the methodology page. Cached input is priced at the cached rate, and reasoning tokens at the worse of the reasoning and output rates.
What you would be replacing
| Intelligence | 1230 |
|---|---|
| Context window | 32K |
| Max output | 29K |
| Input modes | text |
| Tool use | no |
| Extended reasoning | no |
Preserving recorded capabilities requires matching or beating these fields. Full record for Qwen2.5 Coder 32B Instruct, or check Qwen2.5 Coder 32B Instruct against the whole catalogue.
Recorded capabilities preserved 65
Each option has a higher score or a lower price, with neither dimension worse under the listed workloads. It matches or beats Qwen2.5 Coder 32B Instruct on recorded context, maximum output, input modes, tool use and extended reasoning.
| # | Model | Intelligence | $/M | Holds under |
|---|---|---|---|---|
| 1 | MiMo-V2.6-Pro Xiaomi | 1491 +261 | $0.540 |
|
| 2 | GLM 5.3 Flash Z.ai | 1469.6 +239.6 | $0.238 |
|
| 3 | DeepSeek V4.1 Flash DeepSeek | 1462.5 +232.5 | $0.182 |
|
| 4 | MiMo-V2.5-Pro Xiaomi | 1465 +235 | $0.544 |
|
| 5 | MiMo-V2.6-Flash Xiaomi | 1456.4 +226.4 | $0.175 |
|
| 6 | Qwen3.7 Plus Qwen | 1454.6 +224.6 | $0.560 |
|
| 7 | Hy3 Tencent | 1440.7 +210.7 | $0.231 |
|
| 8 | Qwen3.8 27B Qwen | 1440.7 +210.7 | $0.562 |
|
| 9 | Gemma 4 26B A4B Google | 1433.9 +203.9 | $0.127 |
|
| 10 | DeepSeek V4 Flash 0423 DeepSeek | 1432.1 +202.1 | $0.113 |
|
| 11 | Qwen3.6 Plus Qwen | 1436.7 +206.7 | $0.731 |
|
| 12 | GLM 4.7 Z.ai | 1435.5 +205.5 | $0.738 |
|
| 13 | MiniMax M3 MiniMax | 1432.1 +202.1 | $0.485 |
|
| 14 | MiMo-V2.5 Xiaomi | 1427.5 +197.5 | $0.175 |
|
| 15 | GPT-5.6 Luna OpenAI | 1431 +201 | $0.450 |
|
| 16 | DeepSeek V3.2 DeepSeek | 1424.5 +194.5 | $0.290 |
|
| 17 | DeepSeek V3.2 Exp DeepSeek | 1422.4 +192.4 | $0.305 |
|
| 18 | Qwen3 VL 235B A22B Instruct Qwen | 1420.1 +190.1 | $0.370 |
|
| 19 | Qwen3 Next 80B A3B Instruct Qwen | 1417.3 +187.3 | $0.268 |
|
| 20 | DeepSeek V3.1 DeepSeek | 1419.7 +189.7 | $0.453 |
|
| 21 | DeepSeek V3.1 Terminus DeepSeek | 1417.1 +187.1 | $0.453 |
|
| 22 | Gemini 3.1 Flash Lite Preview Google | 1415.7 +185.7 | $0.563 |
|
| 23 | Qwen3.5-122B-A10B Qwen | 1417.1 +187.1 | $0.715 |
|
| 24 | Inkling Small Thinking Machines | 1413.7 +183.7 | $0.638 |
|
| 25 | Qwen3.5-27B Qwen | 1408.9 +178.9 | $0.536 |
|
| 26 | Step 3.5 Flash StepFun | 1403 +173 | $0.150 |
|
| 27 | MiniMax M2.7 MiniMax | 1404.6 +174.6 | $0.525 |
|
| 28 | Qwen3.5-Flash Qwen | 1397 +167 | $0.114 |
|
| 29 | Qwen3.5-35B-A3B Qwen | 1394.4 +164.4 | $0.355 |
|
| 30 | GPT-6 Luna OpenAI | 1391.5 +161.5 | $0.200 |
|
| 31 | Qwen3 30B A3B Instruct 2507 Qwen | 1384.1 +154.1 | $0.143 |
|
| 32 | GLM 4.5 Air Z.ai | 1383.9 +153.9 | $0.310 |
|
| 33 | Solar Pro 4 Upstage | 1386.2 +156.2 | $0.525 |
|
| 34 | GLM 4.6V Z.ai | 1376.8 +146.8 | $0.450 |
|
| 35 | DeepSeek V3 0324 DeepSeek | 1375 +145 | $0.438 |
|
| 36 | GPT-5.4 Nano OpenAI | 1372.1 +142.1 | $0.463 |
|
| 37 | gpt-oss-120b OpenAI | 1365.4 +135.4 | $0.065 |
|
| 38 | GPT-5 Mini OpenAI | 1373.2 +143.2 | $0.688 |
|
| 39 | Qwen3 Next 80B A3B Thinking Qwen | 1368.3 +138.3 | $0.413 |
|
| 40 | Gemma 3 27B Google | 1357.8 +127.8 | $0.100 |
|
| 41 | MiniMax M2.5 MiniMax | 1359.3 +129.3 | $0.521 |
|
| 42 | Mercury 2 Inception | 1355.2 +125.2 | $0.375 |
|
| 43 | GLM 4.7 Flash Z.ai | 1350.9 +120.9 | $0.145 |
|
| 44 | Trinity Large Thinking Arcee | 1340 +110 | $0.388 |
|
| 45 | MiniMax M2 MiniMax | 1339.9 +109.9 | $0.525 |
|
| 46 | GPT-4.1 Mini OpenAI | 1340.4 +110.4 | $0.700 |
|
| 47 | GPT-5 Nano OpenAI | 1320 +90 | $0.138 |
|
| 48 | Granite 4.2 8B IBM | 1317.8 +87.8 | $0.108 |
|
| 49 | gpt-oss-20b OpenAI | 1287.7 +57.7 | $0.036 |
|
| 50 | GPT-4.1 Nano OpenAI | 1284.8 +54.8 | $0.175 |
|
| 51 | GLM 5.3 Z.ai | 1471.4 +241.4 | $0.346 summarise |
|
| 52 | GLM 5.2 Z.ai | 1470.4 +240.4 | $0.422 summarise |
|
| 53 | Kimi K2.6 Moonshot AI | 1455.5 +225.5 | $0.460 summarise |
|
| 54 | GLM 5 Z.ai | 1446.3 +216.3 | $0.617 summarise |
|
| 55 | DeepSeek V4 Pro 0423 DeepSeek | 1445.2 +215.2 | $0.502 summarise |
|
| 56 | Kimi K2.5 Moonshot AI | 1445.2 +215.2 | $0.578 summarise |
|
| 57 | Qwen3.5 397B A17B Qwen | 1438 +208 | $0.488 summarise |
|
| 58 | Gemini 3.5 Flash Lite Google | 1434.7 +204.7 | $0.333 summarise |
|
| 59 | GLM 4.5 Z.ai | 1430.3 +200.3 | $0.540 summarise |
|
| 60 | R1 0528 DeepSeek | 1427.9 +197.9 | $0.584 summarise |
|
| 61 | Gemini 2.5 Flash Google | 1417 +187 | $0.333 summarise |
|
| 62 | Qwen3 235B A22B Thinking 2507 Qwen | 1415.2 +185.2 | $0.334 summarise |
|
| 63 | Qwen3 VL 235B A22B Thinking Qwen | 1400.7 +170.7 | $0.580 summarise |
|
| 64 | Nova 2 Lite Amazon | 1361.8 +131.8 | $0.410 summarise |
|
| 65 | MiniMax M1 MiniMax | 1342.8 +112.8 | $0.490 summarise |
|
Alternatives with recorded capability losses 16
These improve score or price without worsening the other under the listed workloads, but reduce at least one recorded capability. Read the last column before switching.
| Model | Intelligence | $/M | Holds under | Recorded capability loss |
|---|---|---|---|---|
| Gemma 4 31B Google | 1443.4 +213.4 | $0.205 |
|
|
| Qwen3 235B A22B Instruct 2507 Qwen | 1419.3 +189.3 | $0.205 |
|
|
| Qwen3 32B Qwen | 1340.1 +110.1 | $0.130 |
|
|
| Gemma 3 12B Google | 1334.2 +104.2 | $0.075 |
|
|
| DeepSeek V3 DeepSeek | 1332.5 +102.5 | $0.500 |
|
|
| Qwen3 30B A3B Qwen | 1316.6 +86.6 | $0.215 |
|
|
| Gemma 3 4B Google | 1290.8 +60.8 | $0.063 |
|
|
| GPT-4o-mini (2024-07-18) OpenAI | 1286.4 +56.4 | $0.263 |
|
|
| Llama 3.3 70B Instruct Meta | 1274.1 +44.1 | $0.201 |
|
|
| Qwen2.5 72B Instruct Qwen | 1269 +39 | $0.370 |
|
|
| Llama 3.1 70B Instruct Meta | 1260.9 +30.9 | $0.720 |
|
|
| Mistral Small 3 Mistral | 1233.6 tie | $0.058 |
|
|
| Gemma 2 27B Google | 1231.3 tie | $0.650 |
|
|
| GLM 4.6 Z.ai | 1439.9 +209.9 | $0.396 summarise |
|
|
| Qwen3 235B A22B Qwen | 1366.6 +136.6 | $0.523 summarise |
|
|
| GLM 4.5V Z.ai | 1332.8 +102.8 | $0.520 summarise |
|
|
What this compares, and what it leaves out
- Quality is LMArena Elo, used under CC BY 4.0 from the official dataset. The 95% confidence interval on a difference between two scores is about ±10.47 points, so a gap smaller than that is marked tie rather than an improvement — see significance bands.
- 204 further models at this delivery mode carry a price but no independent score. They are absent from the comparison in both directions — unrated is not a zero, and an unmeasured model is neither an alternative nor a worse buy.
- Retired models are never offered as an alternative, and a model only competes against its own delivery mode: batch trades latency for price, so it is not a like-for-like swap.
- Nothing here measures latency, throughput, rate limits or how a model behaves on your prompts. Two models with the same index score are not interchangeable.
- Models with no higher-scoring or cheaper alternative that is no worse on the other axis are on the value frontier. Models with such an alternative are listed here.