Different weight classes. DeepSeek V4 is a frontier flagship; gpt-oss tops out at 117B parameters engineered for single-GPU and laptop deployment. If your constraint is hardware you own, gpt-oss (especially the 20B) is the accessible choice from a US vendor. If your constraint is capability, V4 isn't a fair fight — it simply wins.
gpt-oss vs DeepSeek at a glance
| gpt-oss | DeepSeek V4 | |
|---|---|---|
| Vendor | OpenAI (United States) | DeepSeek (China) |
| Open weights | Yes — downloadable | Yes — downloadable |
| License | Apache 2.0 — permissive, patent-granting, free for commercial use | MIT — weights free to download, self-host, fine-tune, and use commercially |
| Parameters | 117B (gpt-oss-120b) / 21B (gpt-oss-20b) (5.1B / 3.6B active) | 1.6T (49B active) |
| Context window | 128K tokens | 1M tokens |
| Modalities | text | text |
| Pricing | Varies by hosting provider (open weights) | $1.74 in / $3.48 out per 1M tokens |
| Released | 2025-08-05 | 2026-04-24 |
Specs and pricing verified July 2026.
About gpt-oss
gpt-oss is OpenAI's return to open weights — its first since GPT-2. The August 2025 release shipped two MoE reasoning models under Apache 2.0: gpt-oss-120b (117B total, 5.1B active, single-80GB-GPU serving in native MXFP4) and gpt-oss-20b (21B total, runs on a 16GB consumer GPU), both with configurable reasoning effort and 128K context.
Strategically it validated the open-weight thesis from the least likely vendor, and practically the 20B became one of the most-run local models in the world — the default 'serious model on a laptop.' For pure capability the 2026 Chinese frontier models outclass it, but for accessible, US-origin, Apache-2.0 deployment it remains a staple.
Full specs, benchmarks, and hardware guidance: the gpt-oss page.
About DeepSeek V4
DeepSeek V4 Pro is the flagship of DeepSeek's V4 family, released on April 24, 2026, and the strongest argument yet that open-weight models compete at the frontier. It is a 1.6-trillion-parameter mixture-of-experts model that activates just 49B parameters per token, pairs a 1M-token context window with up to 384K tokens of output, and ships under a plain MIT license — no usage thresholds, no acceptable-use gate.
The headline engineering story is the attention stack. V4 combines Compressed Sparse Attention (CSA), which compresses KV entries 4× along the sequence with softmax-gated pooling, with Heavily Compressed Attention (HCA) at 128× compression. The practical result: at 1M-token context, V4 Pro needs roughly 27% of the single-token inference FLOPs and 10% of the KV cache of DeepSeek-V3.2. Long context stopped being a luxury and became the default across DeepSeek's services.
Full specs, benchmarks, and hardware guidance: the DeepSeek V4 page.
Choosing between them
Choose gpt-oss for:
- Local deployment on consumer hardware (20B on a 16GB GPU)
- US-origin open weights under Apache 2.0 for policy-constrained buyers
- Configurable reasoning-effort workloads
Choose DeepSeek V4 for:
- Frontier-level reasoning and coding on an open license
- Very long documents and repositories (1M-token context as the default)
- Teams that need Anthropic/OpenAI API compatibility with open-weight economics
Frequently asked questions
Is gpt-oss better than DeepSeek V4?
Different weight classes. DeepSeek V4 is a frontier flagship; gpt-oss tops out at 117B parameters engineered for single-GPU and laptop deployment. If your constraint is hardware you own, gpt-oss (especially the 20B) is the accessible choice from a US vendor. If your constraint is capability, V4 isn't a fair fight — it simply wins.
Which is cheaper: gpt-oss or DeepSeek V4?
gpt-oss: Varies by hosting provider (open weights). DeepSeek V4: $1.74 in / $3.48 out per 1M tokens. Both are open-weight, so self-hosting costs depend on your hardware and utilization.
Can I self-host gpt-oss and DeepSeek V4?
gpt-oss: yes — weights are published under Apache 2.0. DeepSeek V4: yes — weights are published under MIT.