GPT OSS 20B vs Qwen2.5 Coder 32B Instruct
GPT OSS 20B leads llmrun Score 96.4 to 81.9; both fit 24 GB at Q4_K_M; GPT OSS 20B is 5.9× faster on RTX 4090 (~190 vs ~32 tok/s).
Model A · OpenAI
GPT OSS 20B20.9B · 4.2B active · 128K context · Apache 2.0 · Aug 2025
Model B · Alibaba
Qwen2.5 Coder 32B Instruct32.8B · 32K context · Apache 2.0 · Nov 2024
Choose GPT OSS 20B if you want…
Higher llmrun Score
96.4 vs 81.9
Fits a 12 GB GPU
IQ4_XS · 11.8 GB; Qwen2.5 Coder 32B Instruct needs 13.6 GB at its smallest quant
5.9× faster on RTX 4090
~190 vs ~32 tok/s
Lighter on memory
12.9 vs 20.5 GB at Q4_K_M
Longer context window
128K vs 32K tokens
Newer release
Aug 2025 vs Nov 2024
Choose Qwen2.5 Coder 32B Instruct if you want…
Also does code
GPT OSS 20B doesn't list it
On your hardware
Each model at its default quant (Q4_K_M), a smaller one when it must. Estimates, ±20%.
8 GB
RTX 4060 · 8 GB tier
GPT OSS 20B
IQ2_M · ~103 tok/s
7.6 GB · up to 32Kfits
Qwen2.5 Coder 32B Instruct
Doesn't fit
smallest quant needs 13.6 GB
12 GB
RTX 3060 12GB · 12 GB tier
GPT OSS 20B
IQ4_XS · ~89 tok/s
11.8 GB · up to 16Kfits
Qwen2.5 Coder 32B Instruct
Doesn't fit
smallest quant needs 13.6 GB
16 GB
RTX 5060 Ti 16GB · 16 GB tier
GPT OSS 20B
Q4_K_M · ~98 tok/s
13.1 GB · up to 128Kmore headroom
Qwen2.5 Coder 32B Instruct
IQ2_M · ~25 tok/s
15.7 GB · up to 16K
24 GBreference
RTX 4090 · 24 GB tier
GPT OSS 20B
Q4_K_M · ~190 tok/s
13.1 GB · up to 128Kmore headroom
Qwen2.5 Coder 32B Instruct
Q4_K_M · ~32 tok/stight
24.3 GB · up to 8K
32 GB
RTX 5090 · 32 GB tier
GPT OSS 20B
Q4_K_M · ~282 tok/s
13.1 GB · up to 128Kfaster
Qwen2.5 Coder 32B Instruct
Q4_K_M · ~57 tok/s
24.3 GB · up to 32K
48 GB
RTX A6000 · 48 GB tier
GPT OSS 20B
Q4_K_M · ~154 tok/s
13.1 GB · up to 128Kfaster
Qwen2.5 Coder 32B Instruct
Q4_K_M · ~24 tok/s
24.3 GB · up to 32K
Mac 32 GB
Mac Mini M4 · Macs
GPT OSS 20B
Q4_K_M · ~28 tok/s
13.1 GB · up to 128Kfaster
Qwen2.5 Coder 32B Instruct
Q4_K_M · ~4 tok/s
24.3 GB · up to 16K
Mac 64 GB
Mac Studio M4 Max · Macs
GPT OSS 20B
Q4_K_M · ~86 tok/s
13.1 GB · up to 128Kfaster
Qwen2.5 Coder 32B Instruct
Q4_K_M · ~19 tok/s
24.3 GB · up to 32K
Mac 128 GB
Mac Studio M4 Max · Macs
GPT OSS 20B
Q4_K_M · ~86 tok/s
13.1 GB · up to 128Kfaster
Qwen2.5 Coder 32B Instruct
Q4_K_M · ~19 tok/s
24.3 GB · up to 32K
Reference GPU: RTX 4090. Tinted cell = the better pick on that machine (fits, more headroom, or at least 10% faster).
Overall
GPT OSS 20B
Qwen2.5 Coder 32B Instruct
Coding
GPT OSS 20B
Qwen2.5 Coder 32B Instruct
no data
Agents
GPT OSS 20B
no data
Qwen2.5 Coder 32B Instruct
Science
GPT OSS 20B
Qwen2.5 Coder 32B Instruct
no data
Reasoning
GPT OSS 20B
Qwen2.5 Coder 32B Instruct
no data
GPT OSS 20B · #69 of 106 open models · #16 of 36 that fit 24 GB
Qwen2.5 Coder 32B Instruct · #86 of 106 open models · #24 of 36 that fit 24 GB
15 more boards (other shared and single-model boards)
GPT OSS 20B
1317.3#98/188 open
ArenaLMArena · Oct 2026
Qwen2.5 Coder 32B Instruct
1270.6#123/188 open
ArenaLMArena · Oct 2026
Only GPT OSS 20B (10)
- GPQA Diamond60.8%
- AIME 2024/202565.3%
- Terminal-Bench3.4%
- MMLU-Pro73.6%
- SciCode38.9%
- WeirdML40.9%
- CritPt1.4%
- DTBench68.0%
- LMCA14.5%
- ALE-Bench566.0
Only Qwen2.5 Coder 32B Instruct (4)
- SWE-bench Verified47.0%
- Aider Polyglot16.4%
- SWE-bench Bash Only9.0%
- GSM8K93.0%
VRAM by quantization
Memory to load the model and 2K context; below it, the total at 32K and 128K context. Lighter side in bold.
Q2_K
GPT OSS 20B
9.3 GB11.5 GB file
32K 9.8 GB · 128K 11.5 GB
Qwen2.5 Coder 32B Instruct
14.8 GB12.3 GB file
32K 22.8 GB · 128K —
Q3_K_S
GPT OSS 20B
9.5 GB11.5 GB file
32K 10.0 GB · 128K 11.7 GB
Qwen2.5 Coder 32B Instruct
15.2 GB14.4 GB file
32K 23.2 GB · 128K —
Q3_K_M
GPT OSS 20B
10.6 GB11.5 GB file
32K 11.1 GB · 128K 12.8 GB
Qwen2.5 Coder 32B Instruct
16.8 GB15.9 GB file
32K 24.9 GB · 128K —
Q4_0
GPT OSS 20B
10.8 GB11.5 GB file
32K 11.3 GB · 128K 13.0 GB
Qwen2.5 Coder 32B Instruct
17.2 GB18.6 GB file
32K 25.3 GB · 128K —
Q4_K_M
GPT OSS 20B
12.9 GB11.6 GB file
32K 13.4 GB · 128K 15.1 GB
Qwen2.5 Coder 32B Instruct
20.5 GB19.9 GB file
32K 28.6 GB · 128K —
Q5_K_M
GPT OSS 20B
15.3 GB11.7 GB file
32K 15.8 GB · 128K 17.5 GB
Qwen2.5 Coder 32B Instruct
24.2 GB23.3 GB file
32K 32.2 GB · 128K —
Q6_K
GPT OSS 20B
17.6 GB12.0 GB file
32K 18.1 GB · 128K 19.8 GB
Qwen2.5 Coder 32B Instruct
27.9 GB26.9 GB file
32K 35.9 GB · 128K —
Q8_0
GPT OSS 20B
21.3 GB~20.9 GB file
32K 21.8 GB · 128K 23.5 GB
Qwen2.5 Coder 32B Instruct
33.6 GB34.8 GB file
32K 41.7 GB · 128K —
19 more quants
IQ2_XXS
GPT OSS 20B
6.1 GB~5.8 GB file
32K 6.6 GB · 128K 8.3 GB
Qwen2.5 Coder 32B Instruct
9.9 GB9.0 GB file
32K 17.9 GB · 128K —
IQ2_XS
GPT OSS 20B
6.7 GB~6.3 GB file
32K 7.2 GB · 128K 8.9 GB
Qwen2.5 Coder 32B Instruct
10.7 GB10.0 GB file
32K 18.7 GB · 128K —
IQ2_S
GPT OSS 20B
6.9 GB~6.5 GB file
32K 7.4 GB · 128K 9.1 GB
Qwen2.5 Coder 32B Instruct
11.1 GB10.4 GB file
32K 19.1 GB · 128K —
IQ2_M
GPT OSS 20B
7.4 GB~7.1 GB file
32K 7.9 GB · 128K 9.6 GB
Qwen2.5 Coder 32B Instruct
11.9 GB11.3 GB file
32K 19.9 GB · 128K —
IQ3_XXS
GPT OSS 20B
8.5 GB11.6 GB file
32K 9.0 GB · 128K 10.7 GB
Qwen2.5 Coder 32B Instruct
13.5 GB12.8 GB file
32K 21.6 GB · 128K —
Q2_K_S
GPT OSS 20B
8.7 GB12.1 GB file
32K 9.2 GB · 128K 10.9 GB
Qwen2.5 Coder 32B Instruct
not listed
IQ3_XS
GPT OSS 20B
9.0 GB11.6 GB file
32K 9.5 GB · 128K 11.2 GB
Qwen2.5 Coder 32B Instruct
14.4 GB13.7 GB file
32K 22.4 GB · 128K —
IQ3_S
GPT OSS 20B
9.3 GB12.1 GB file
32K 9.8 GB · 128K 11.5 GB
Qwen2.5 Coder 32B Instruct
14.8 GB14.4 GB file
32K 22.8 GB · 128K —
IQ3_M
GPT OSS 20B
9.8 GB11.6 GB file
32K 10.3 GB · 128K 12.0 GB
Qwen2.5 Coder 32B Instruct
15.6 GB14.8 GB file
32K 23.6 GB · 128K —
Q3_K_L
GPT OSS 20B
11.1 GB11.5 GB file
32K 11.6 GB · 128K 13.3 GB
Qwen2.5 Coder 32B Instruct
17.6 GB17.2 GB file
32K 25.7 GB · 128K —
IQ4_XS
GPT OSS 20B
11.6 GB11.6 GB file
32K 12.1 GB · 128K 13.8 GB
Qwen2.5 Coder 32B Instruct
18.5 GB17.7 GB file
32K 26.5 GB · 128K —
IQ4_NL
GPT OSS 20B
12.1 GB11.6 GB file
32K 12.6 GB · 128K 14.3 GB
Qwen2.5 Coder 32B Instruct
19.3 GB18.7 GB file
32K 27.3 GB · 128K —
Q4_1
GPT OSS 20B
12.1 GB11.6 GB file
32K 12.6 GB · 128K 14.3 GB
Qwen2.5 Coder 32B Instruct
not listed
Q4_K_S
GPT OSS 20B
12.1 GB11.6 GB file
32K 12.6 GB · 128K 14.3 GB
Qwen2.5 Coder 32B Instruct
19.3 GB18.8 GB file
32K 27.3 GB · 128K —
Q4_K_L
GPT OSS 20B
13.2 GB11.9 GB file
32K 13.7 GB · 128K 15.4 GB
Qwen2.5 Coder 32B Instruct
20.9 GB20.4 GB file
32K 29.0 GB · 128K —
Q5_0
GPT OSS 20B
not listed
Qwen2.5 Coder 32B Instruct
21.3 GB22.6 GB file
32K 29.4 GB · 128K —
Q5_K_S
GPT OSS 20B
14.8 GB11.7 GB file
32K 15.3 GB · 128K 17.0 GB
Qwen2.5 Coder 32B Instruct
23.4 GB22.6 GB file
32K 31.4 GB · 128K —
Q5_K_L
GPT OSS 20B
15.5 GB11.9 GB file
32K 16.0 GB · 128K 17.7 GB
Qwen2.5 Coder 32B Instruct
24.6 GB23.7 GB file
32K 32.6 GB · 128K —
BF16
GPT OSS 20B
42.2 GB~41.8 GB file
32K 42.7 GB · 128K 44.4 GB
Qwen2.5 Coder 32B Instruct
66.4 GB~65.5 GB file
32K 74.4 GB · 128K —
GPT OSS 20B
Ollama · Q4_K_M
ollama run hf.co/unsloth/gpt-oss-20b-GGUF:Q4_K_MQwen2.5 Coder 32B Instruct
Ollama · Q4_K_M
ollama run hf.co/Qwen/Qwen2.5-Coder-32B-Instruct-GGUF:Q4_K_M| Spec | GPT OSS 20B | Qwen2.5 Coder 32B Instruct |
|---|---|---|
| Publisher | OpenAI | Alibaba |
| Parameters | 20.9B | 32.8B |
| Active parameters | 4.2B per token | — |
| Architecture | GptOssForCausalLM | Qwen2ForCausalLM |
| Context | 128K tokens | 32K tokens |
| Modalities | Chat, Function-calling | Chat, Code, Function-calling |
| License | Apache 2.0 | Apache 2.0 |
| Released | Aug 2025 | Nov 2024 |
Frequently Asked Questions
Which is better, GPT OSS 20B or Qwen2.5 Coder 32B Instruct?
GPT OSS 20B leads llmrun Score 96.4 to 81.9; both fit 24 GB at Q4_K_M; GPT OSS 20B is 5.9× faster on RTX 4090 (~190 vs ~32 tok/s). Choose GPT OSS 20B for higher llmrun Score and fits a 12 GB GPU; choose Qwen2.5 Coder 32B Instruct for also does code.
Which needs less VRAM, GPT OSS 20B or Qwen2.5 Coder 32B Instruct?
At Q4_K_M, GPT OSS 20B requires 12.9 GB while Qwen2.5 Coder 32B Instruct needs 20.5 GB, making GPT OSS 20B the easier model to run on your local setup.
Which is faster on RTX 4090?
Estimated decode speed on RTX 4090 is about 190 tok/s for GPT OSS 20B (Q4_K_M) and 32 tok/s for Qwen2.5 Coder 32B Instruct (Q4_K_M), so GPT OSS 20B is faster. Estimates, ±20%.
Can I run GPT OSS 20B or Qwen2.5 Coder 32B Instruct on a 16 GB GPU?
GPT OSS 20B fits at Q4_K_M; Qwen2.5 Coder 32B Instruct fits at IQ2_M.
Which has a longer context window, GPT OSS 20B or Qwen2.5 Coder 32B Instruct?
GPT OSS 20B supports 131,072 tokens and Qwen2.5 Coder 32B Instruct supports 32,768 tokens.