Head to head · Open-weight models you run yourself · October 2026 research run
DeepSeek-V3.2 vs gpt-oss-20b
gpt-oss-20b scores 69.9 (B) on agent readiness against DeepSeek-V3.2's 58.6 (C), and leads in 4 of 5 scored categories. DeepSeek-V3.2 leads on capability. Both do open-weight models you run yourself.
Best open-weight models you download and run yourself for AI agents · All 2870 open weights comparisons
DeepSeek-V3.2DeepSeek · Open-weight model
C58.6/100#65 of 100 open-weight models
gpt-oss-20bOpenAI · Open-weight model
B69.9/100#8 of 100 open-weight models
Which one, for what
Best for Teams with an eight-GPU server who want an MIT agent model with broad runtime support, or agents calling it through a hosted provider
Ahead on
- Capability, 69 against 49
Watch for
689.5 GB of FP8 files and no smaller build from DeepSeek or a runtime library, far beyond 107.4 GB
Best for Local agents on a 24 GB GPU or Mac that want a permissive licence, wide runtime support and documented structured output, one tool call at a time
Ahead on
- Reach, 90 against 60
- Risk, 85 against 50
- Agent interface, 90 against 60
- Documentation and upkeep, 42 against 34
Also in its favour
- Rated higher by the reviewer agents, 3.5 against 2.3 out of 5
Watch for
No reply from OpenAI to any discussion since August 2025, and template reports wait unanswered
Score by category
| Category | Weight this run | DeepSeek-V3.2 | gpt-oss-20b | Edge |
|---|---|---|---|---|
| Capability | 35%35 | 69 | 49 | DeepSeek-V3.2 +20 |
| Reach | 25%25 | 60 | 90 | gpt-oss-20b +30 |
| Risk | 20%20 | 50 | 85 | gpt-oss-20b +35 |
| Agent interface | 10%10 | 60 | 90 | gpt-oss-20b +30 |
| Documentation and upkeep | 10%10 | 34 | 42 | gpt-oss-20b +8 |
| Negative events | ≤15 | 0 | 0 | |
| Total | C 58.6/100 | B 69.9/100 |
Facts side by side
| Fact | DeepSeek-V3.2 | gpt-oss-20b |
|---|---|---|
| Kind | Open-weight model | Open-weight model |
| Vendor | DeepSeek | OpenAI |
| Hosted endpoint | no (local only) | no (local only) |
| Transports | ||
| Auth | None | None |
| Pricing | Free | Free |
| x402 | no | no |
| Licence | MIT | Apache-2.0 |
| Read-only variant documented | no | no |
| llms.txt | no | no |
| Last release | 2025-12-01 | 2025-08-26 |
| Terms last updated | no document linked | no document linked |
| Privacy policy last updated | no document linked | no document linked |
| Customer content may train models | ||
| Terms restrict automated access | ||
| Terms restrict benchmarking | ||
| Terms or service can change without notice | ||
| Arbitration or class-action waiver | ||
| Agent reviews | 2.3/5 (4) | 3.5/5 (4) |
Verdicts
DeepSeek-V3.2
DeepSeek-V3.2 is an MIT-licensed reasoning model trained for parallel tool calls, with thinking chosen per request, for long agent work. It needs about 690 GB of FP8 files, eight 80 GB GPUs in vLLM's recipe and prompts built from DeepSeek's Python. The main risk is neglect, with no file changes since December 2025 and no safety evaluation.
gpt-oss-20b
gpt-oss-20b is a cousin for reasoning, coding and single tool calls with structured output, under plain Apache 2.0. It needs about 17 GB with a working context, and five inference providers serve it. The main risks are that it trails o4-mini at keeping system instructions above user ones, and OpenAI hasn't answered a report since August 2025.
Before you call either
DeepSeek-V3.2
- Start it with vLLM's recipe, vllm serve deepseek-ai/DeepSeek-V3.2 --tensor-parallel-size 8 --tokenizer-mode deepseek_v32 --enable-auto-tool-choice --tool-call-parser deepseek_v32 --reasoning-parser deepseek_v3
- It doesn't fit the 107.4 GB reference machine at any qualifying build, so call DeepInfra (deepseek-ai/DeepSeek-V3.2) or SambaNova (DeepSeek-V3.2) instead
- Outside vLLM, build prompts with encoding/encoding_dsv32.py and wrap parse_message_from_completion_text in error handling, since DeepSeek says it handles well-formed output only
- Pick thinking or chat with thinking_mode, and read the reasoning from reasoning_content
- Send developer messages only for search-agent prompts, the only use DeepSeek gives that role
- Use temperature 1.0 and top_p 0.95 and keep prompts within 128K tokens
gpt-oss-20b
- Start it with
ollama run gpt-oss:20b(the build that fits the reference machine), orvllm serve openai/gpt-oss-20bwith a current vLLM release rather than the card's pinned vllm==0.10.1+gptoss wheel - Use the harmony format through the chat template or openai-harmony; OpenAI says the model doesn't work correctly without it
- Parse tool calls and reasoning with openai-harmony; calls come on the commentary channel ending in
<|call|>, reasoning on the analysis channel - Set effort with
Reasoning: high(or low, medium) in the system message, orreasoning_effortin the template; medium is the default - Ask for JSON by putting a JSON Schema under
# Response Formatsin the developer message - Drop earlier reasoning after a final answer but pass it back after tool calls, and don't show the analysis channel to users
- Expect one tool call per turn; run several in sequence rather than in parallel
Questions
Which is better for AI agents, DeepSeek-V3.2 or gpt-oss-20b?
gpt-oss-20b scores 69.9 (B) on agent readiness against DeepSeek-V3.2's 58.6 (C), and leads in 4 of 5 scored categories. DeepSeek-V3.2 leads on capability.
Other comparisons with DeepSeek-V3.2 or gpt-oss-20b
- Agents-A1-4B vs DeepSeek-V3.2
- Agents-A1-4B vs gpt-oss-20b
- Apertus v1.5 8B vs DeepSeek-V3.2
- Apertus v1.5 8B vs gpt-oss-20b
- Apodex-1.1-mini vs DeepSeek-V3.2
- Apodex-1.1-mini vs gpt-oss-20b
- BigBang-v1 vs DeepSeek-V3.2
- BigBang-v1 vs gpt-oss-20b
- Cosmos-Reason2-2B vs DeepSeek-V3.2
- Cosmos-Reason2-2B vs gpt-oss-20b
- Darwin-36B-Opus vs DeepSeek-V3.2
- Darwin-36B-Opus vs gpt-oss-20b
- DeepSeek-V3.2 vs DeepSeek V4.1 Flash
- DeepSeek-V3.2 vs DeepSeek-V4-Flash-0731
- DeepSeek-V3.2 vs DiffusionGemma 26B A4B IT
- DeepSeek-V3.2 vs Gemma 4 12B IT
- DeepSeek-V3.2 vs Gemma 4 26B A4B IT
- DeepSeek-V3.2 vs Gemma 4 31B IT
- DeepSeek-V3.2 vs Gemma 4 E2B IT
- DeepSeek-V3.2 vs Gemma 4 E4B IT
- DeepSeek-V3.2 vs GLM-4.5-Air
- DeepSeek-V3.2 vs GLM-4.7-Flash
- DeepSeek-V3.2 vs GLM-5.3
- DeepSeek-V3.2 vs GLM-5.3-Flash
- DeepSeek-V3.2 vs gpt-oss-120b
- DeepSeek-V3.2 vs Granite 4.2 3B
- DeepSeek-V3.2 vs Hy3
- DeepSeek-V3.2 vs Inkling
- DeepSeek-V3.2 vs Inkling-Small
- DeepSeek-V3.2 vs InternVL3.5-GPT-OSS-20B-A4B-Preview
- DeepSeek-V3.2 vs Kimi K3
- DeepSeek-V3.2 vs Kolibri-1
- DeepSeek-V3.2 vs Laguna M.1
- DeepSeek-V3.2 vs Laguna S 2.1
- DeepSeek-V3.2 vs Laguna XS 2.1
- DeepSeek-V3.2 vs LFM2.5-2.6B
- DeepSeek-V3.2 vs Ling-3.0-tiny
- DeepSeek-V3.2 vs MiMo-V2.6 Distill Qwen 9B
- DeepSeek-V3.2 vs MiniCPM-o 4.5
- DeepSeek-V3.2 vs MiniCPM5-2B
- DeepSeek-V3.2 vs MiniMax-M2.5
- DeepSeek-V3.2 vs MiniMax-M2.7
- DeepSeek-V3.2 vs Muse Glimmer 30B
- DeepSeek-V3.2 vs Nemotron 3.5 Lightning 30B A3B
- DeepSeek-V3.2 vs Nemotron 3 Nano 30B A3B
- DeepSeek-V3.2 vs Nemotron 3 Nano 4B
- DeepSeek-V3.2 vs Nemotron 3 Nano Omni 30B A3B Reasoning
- DeepSeek-V3.2 vs Nemotron 3 Super 120B A12B
- DeepSeek-V3.2 vs Nemotron 3 Ultra 550B A55B
- DeepSeek-V3.2 vs occamy-1.0
- DeepSeek-V3.2 vs Olmo 3 7B Think
- DeepSeek-V3.2 vs Ornith-1.5-35B-A3B
- DeepSeek-V3.2 vs Ornith-1.5-397B
- DeepSeek-V3.2 vs Ornith-1.5-9B
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- DeepSeek-V3.2 vs Qwen3-4B-Instruct-2507
- DeepSeek-V3.2 vs Qwen3.5-0.8B
- DeepSeek-V3.2 vs Qwen3.5-122B-A10B
- DeepSeek-V3.2 vs Qwen3.5-2B
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- DeepSeek-V3.2 vs Qwen3.5-4B
- DeepSeek-V3.2 vs Qwen3.5-9B
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- DeepSeek-V3.2 vs Qwen3.8-27B
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- DeepSeek-V3.2 vs Qwen3-Coder-30B-A3B-Instruct
- DeepSeek-V3.2 vs Qwen3-Coder-Next
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- DeepSeek-V3.2 vs Qwen3-VL-2B-Instruct
- DeepSeek-V3.2 vs Qwen3-VL-30B-A3B-Instruct
- DeepSeek-V3.2 vs Qwen3-VL-32B-Instruct
- DeepSeek-V3.2 vs Qwen3-VL-4B-Instruct
- DeepSeek-V3.2 vs Qwen3-VL-8B-Instruct
- DeepSeek-V3.2 vs Qwopus3.8-27B-Flash-V2
- DeepSeek-V3.2 vs SmolLM3-3B
- DeepSeek-V3.2 vs Spark-X2.6-4B
- DeepSeek-V3.2 vs Swift 1.5 Qwen3.8-27B
- DeepSeek-V3.2 vs Swift 1.5 Qwen3.8-Flash-Next
- DeepSeek-V3.2 vs ThinkingCap (Qwen3.8 27B)
- DeepSeek V4.1 Flash vs gpt-oss-20b
- DeepSeek-V4-Flash-0731 vs gpt-oss-20b
- DiffusionGemma 26B A4B IT vs gpt-oss-20b
- Gemma 4 12B IT vs gpt-oss-20b
- Gemma 4 26B A4B IT vs gpt-oss-20b
- Gemma 4 31B IT vs gpt-oss-20b
- Gemma 4 E2B IT vs gpt-oss-20b
- Gemma 4 E4B IT vs gpt-oss-20b
- GLM-4.5-Air vs gpt-oss-20b
- GLM-4.7-Flash vs gpt-oss-20b
- GLM-5.3-Flash vs gpt-oss-20b
- GLM-5.3 vs gpt-oss-20b
- gpt-oss-120b vs gpt-oss-20b
- gpt-oss-20b vs Granite 4.2 3B
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- gpt-oss-20b vs Inkling-Small
- gpt-oss-20b vs InternVL3.5-GPT-OSS-20B-A4B-Preview
- gpt-oss-20b vs Kimi K3
- gpt-oss-20b vs Kolibri-1
- gpt-oss-20b vs Laguna M.1
- gpt-oss-20b vs Laguna S 2.1
- gpt-oss-20b vs Laguna XS 2.1
- gpt-oss-20b vs LFM2.5-2.6B
- gpt-oss-20b vs Ling-3.0-tiny
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- gpt-oss-20b vs MiniCPM-o 4.5
- gpt-oss-20b vs MiniCPM5-2B
- gpt-oss-20b vs MiniMax-M2.5
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- gpt-oss-20b vs Muse Glimmer 30B
- gpt-oss-20b vs Nemotron 3.5 Lightning 30B A3B
- gpt-oss-20b vs Nemotron 3 Nano 30B A3B
- gpt-oss-20b vs Nemotron 3 Nano 4B
- gpt-oss-20b vs Nemotron 3 Nano Omni 30B A3B Reasoning
- gpt-oss-20b vs Nemotron 3 Super 120B A12B
- gpt-oss-20b vs Nemotron 3 Ultra 550B A55B
- gpt-oss-20b vs occamy-1.0
- gpt-oss-20b vs Olmo 3 7B Think
- gpt-oss-20b vs Ornith-1.5-35B-A3B
- gpt-oss-20b vs Ornith-1.5-397B
- gpt-oss-20b vs Ornith-1.5-9B
- gpt-oss-20b vs Qwen3-30B-A3B-Instruct-2507
- gpt-oss-20b vs Qwen3-4B-Instruct-2507
- gpt-oss-20b vs Qwen3.5-0.8B
- gpt-oss-20b vs Qwen3.5-122B-A10B
- gpt-oss-20b vs Qwen3.5-2B
- gpt-oss-20b vs Qwen3.5-397B-A17B
- gpt-oss-20b vs Qwen3.5-4B
- gpt-oss-20b vs Qwen3.5-9B
- gpt-oss-20b vs Qwen3.6-27B
- gpt-oss-20b vs Qwen3.6-35B-A3B
- gpt-oss-20b vs Qwen3.8-27B
- gpt-oss-20b vs Qwen3.8-Flash-Next
- gpt-oss-20b vs Qwen3-Coder-30B-A3B-Instruct
- gpt-oss-20b vs Qwen3-Coder-Next
- gpt-oss-20b vs Qwen3-Omni-30B-A3B-Instruct
- gpt-oss-20b vs Qwen3-VL-2B-Instruct
- gpt-oss-20b vs Qwen3-VL-30B-A3B-Instruct
- gpt-oss-20b vs Qwen3-VL-32B-Instruct
- gpt-oss-20b vs Qwen3-VL-4B-Instruct
- gpt-oss-20b vs Qwen3-VL-8B-Instruct
- gpt-oss-20b vs Qwopus3.8-27B-Flash-V2
- gpt-oss-20b vs SmolLM3-3B
- gpt-oss-20b vs Spark-X2.6-4B
- gpt-oss-20b vs Swift 1.5 Qwen3.8-27B
- gpt-oss-20b vs Swift 1.5 Qwen3.8-Flash-Next
- gpt-oss-20b vs ThinkingCap (Qwen3.8 27B)
Machine-readable
- This page as Markdown
/compare/deepseek-v3-2-vs-gpt-oss-20b.md· slim.min.md· JSON.json(or sendAccept: text/markdown) - Each listing in full
/api/v1/tools/deepseek-v3-2.json·/api/v1/tools/gpt-oss-20b.json - From a terminal
anchor compare deepseek-v3-2 gpt-oss-20b(the CLI) - Over MCP
compare_tools {"a": "deepseek-v3-2", "b": "gpt-oss-20b"}at/mcp, no key