DeepSeek V3.2 vs gpt-oss-120b
DeepSeek · China | OpenAI · US · Updated June 2026
Quick verdict
Pick DeepSeek V3.2 for long-context efficiency via deepseek sparse attention (dsa) or agentic tool-use with thinking integrated into tool calls (thinking/non-thinking modes). Pick gpt-oss-120b for self-hostable on a single 80gb h100 gpu via mxfp4 or configurable reasoning depth (low/medium/high). On a tight budget at scale, gpt-oss-120b is the value pick.
DeepSeek V3.2 (DeepSeek, China) and gpt-oss-120b (OpenAI, US) line up two different AI ecosystems against each other — a comparison that is as much about cost philosophy and openness as raw capability. DeepSeek V3.2 is a cost-efficient, open-weight (MIT) 685B-parameter MoE model whose DeepSeek Sparse Attention delivers GPT-5-comparable reasoning with far cheaper long-context inference. gpt-oss-120b is openAI's open-weight 117B-parameter MoE reasoning model (5.1B active) that runs on a single 80GB GPU and approaches o4-mini on reasoning, coding, and tool use. They diverge most on price and coding benchmarks — each quantified below from the models' real specs.
Key differences at a glance
- ▸Context window: both advertise 131K (~197 pages). Tie on paper — test on your own long inputs, since usable recall varies by model.
- ▸Coding: DeepSeek V3.2 leads SWE-Bench Verified by 10.7 points (73.1% vs 62.4%) — a real edge on hard, real-world software tasks.
- ▸Recency: DeepSeek V3.2 is the newer model by about 4 months (released December 1, 2025), usually meaning fresher training data and capabilities.
- ▸Ecosystem: this is a China-vs-US matchup — they differ in pricing philosophy, data-residency options, and tooling ecosystems, not only benchmarks.
Side-by-side specs
| Spec | DeepSeek V3.2 | gpt-oss-120b |
|---|---|---|
| Provider | DeepSeek (China) | OpenAI (US) |
| Released | December 1, 2025 | August 5, 2025 |
| Context window | 131K (~197 pages) | 131K (~197 pages) |
| Price (in/out) | $0.28/$0.42 per 1M tokens | Open weight (self-host / free) |
| Open weight? | Yes — self-hostable | Yes — self-hostable |
| Modalities | text, code | text, code |
| SWE-Bench Verified | 73.1% | 62.4% |
| MRCR v2 @ 1M | Not published | Not published |
Who wins what
Long-context efficiency via DeepSeek Sparse Attention (DSA)
DeepSeek V3.2
gpt-oss-120b is comparatively weak here — 131K context and 5.1B active params trail the largest frontier closed models
Agentic tool-use with thinking integrated into tool calls (thinking/non-thinking modes)
DeepSeek V3.2
It scores 73.1% on SWE-Bench Verified against gpt-oss-120b's 62.4% — a 10.7-point edge on real repository work.
Elite competition math and reasoning (AIME 2025 93.1, Codeforces 2386)
DeepSeek V3.2
A cost-efficient, open-weight (MIT) 685B-parameter MoE model whose DeepSeek Sparse Attention delivers GPT-5-comparable reasoning with far cheaper long-context inference — and it leads SWE-Bench Verified 73.1% to 62.4%.
Self-hostable on a single 80GB H100 GPU via MXFP4
gpt-oss-120b
gpt-oss-120b lists self-hostable on a single 80GB H100 GPU via MXFP4 among its strengths; DeepSeek V3.2 does not.
Configurable reasoning depth (low/medium/high)
gpt-oss-120b
gpt-oss-120b lists configurable reasoning depth (low/medium/high) among its strengths; DeepSeek V3.2 does not.
Agentic tool use, function calling, and code execution
gpt-oss-120b
gpt-oss-120b lists agentic tool use, function calling, and code execution among its strengths; DeepSeek V3.2 does not.
Lowest cost at scale
gpt-oss-120b
Its weights are open, so at volume you pay for your own hardware instead of DeepSeek V3.2's $0.28/$0.42 per 1M tokens.
Which should you pick?
A cost-sensitive startup shipping high volume
→ gpt-oss-120b
At Open weight (self-host / free) it undercuts DeepSeek V3.2, and on millions of tokens that margin decides the monthly bill.
Anyone whose priority is long-context efficiency via deepseek sparse attention (dsa)
→ DeepSeek V3.2
It is specifically built for that.
Anyone whose priority is self-hostable on a single 80gb h100 gpu via mxfp4
→ gpt-oss-120b
That is its strongest area.
An enterprise with regional data-residency rules
→ gpt-oss-120b or DeepSeek V3.2
Origin (China vs US) affects where data is processed and which compliance regime applies — check the provider's terms for your region.
DeepSeek V3.2: where it fits
A cost-efficient, open-weight (MIT) 685B-parameter MoE model whose DeepSeek Sparse Attention delivers GPT-5-comparable reasoning with far cheaper long-context inference. Released December 1, 2025 by DeepSeek, it is built for long-context efficiency via DeepSeek Sparse Attention (DSA), agentic tool-use with thinking integrated into tool calls (thinking/non-thinking modes), elite competition math and reasoning (AIME 2025 93.1, Codeforces 2386), and low-cost, open-weight (MIT) self-hosting.
Its trade-offs are real: superseded - no longer listed on DeepSeek's current pricing page as of August 2026; DeepSeek V4-Flash/V4-Pro are the current models, text-only — no image, audio, or video input, and sWE-Bench Verified (73.1) trails the top closed coding models (Claude 4.5 Sonnet 77.2, Gemini 3 Pro 76.2). At $0.28 in / $0.42 out per million tokens, it sits in the budget price band.
gpt-oss-120b: where it fits
OpenAI's open-weight 117B-parameter MoE reasoning model (5.1B active) that runs on a single 80GB GPU and approaches o4-mini on reasoning, coding, and tool use. Released August 5, 2025 by OpenAI, it is built for self-hostable on a single 80GB H100 GPU via MXFP4, configurable reasoning depth (low/medium/high), agentic tool use, function calling, and code execution, and full chain-of-thought visibility for debugging.
Its trade-offs: text-only, no image, audio, or video input, and 131K context and 5.1B active params trail the largest frontier closed models. As an open-weight model, its running cost is your own hardware rather than a per-token fee.
The bottom line for this matchup
This is less "which is smarter" and more "which ecosystem fits." DeepSeek V3.2 (China) and gpt-oss-120b (US) differ on pricing philosophy, data-residency, and tooling as much as on raw scores. gpt-oss-120b is the cheaper option, which matters at volume. The pragmatic move is to run one real task through both and judge the outputs against your own constraints — including where your data is allowed to be processed.
Want both DeepSeek V3.2 and gpt-oss-120b without two subscriptions? LumiChats gives you these plus 40+ models under one ₹69/day pass (about $1/day) — draft with one, cross-check with the other.
See pricingFrequently asked questions
Is DeepSeek V3.2 or gpt-oss-120b better for coding?
On SWE-Bench Verified, DeepSeek V3.2 scores 73.1% and gpt-oss-120b scores 62.4% — DeepSeek V3.2 has the measurable edge.
Which is cheaper, DeepSeek V3.2 or gpt-oss-120b?
gpt-oss-120b is cheaper — $0.28/$0.42 per 1M tokens vs Open weight (self-host / free).
Which has the bigger context window?
Both advertise 131K (~197 pages). Remember advertised ≠ usable: recall typically degrades before the ceiling.
Can I use both DeepSeek V3.2 and gpt-oss-120b together?
Yes — a multi-model platform like LumiChats gives you DeepSeek V3.2, gpt-oss-120b and 40+ others under one ₹69/day pass (about $1/day), so you can draft with one and cross-check with the other instead of buying two subscriptions.
Which is newer, DeepSeek V3.2 or gpt-oss-120b?
DeepSeek V3.2 — released December 1, 2025, about 4 months after gpt-oss-120b.
Related comparisons
Specifications and benchmarks reflect publicly reported figures as of June 2026 and may change as providers release updates. Always verify on your own workload.