Compare the listed model data for DeepSeek-V3 by DeepSeek and GPT-OSS 120B by Microsoft / OpenAI. Benchmark and pricing records are not a controlled test and may change.
Data last updated: 2026-10-16. Verify current model details with each provider.
What the listed data says
DeepSeek-V3: 128K tokens; GPT-OSS 120B: 128K tokens. The listed context-window values are equal. DeepSeek-V3 is listed for coding, agentic workflows, long-context tasks; GPT-OSS 120B is listed for coding, reasoning, agentic workflows, long-context tasks. Shared catalog benchmark entries: MMLU: DeepSeek-V3 88.5%; GPT-OSS 120B 85.0%; HumanEval: DeepSeek-V3 89.9%; GPT-OSS 120B 88.0%. These records are reference data, not a controlled head-to-head test; verify current pricing and specifications with each provider.
Catalog source links
Use these model and provider links to verify current specifications, pricing, and availability.
The listed context windows are 128K tokens and 128K tokens; the larger is 1× the smaller.
On shared numeric benchmark entries, MMLU: DeepSeek-V3 − GPT-OSS 120B = +3.5 percentage points (88.5% vs 85.0%); HumanEval: DeepSeek-V3 − GPT-OSS 120B = +1.9 percentage points (89.9% vs 88.0%).
The first listed parameter quantities are 671B total (37B active via MoE) and 120B; the larger quantity is 5.59× the smaller. Total and active MoE parameters are not directly equivalent.
A release-date gap cannot be calculated from the catalog values (DeepSeek-V3: December 2024; GPT-OSS 120B: 2025).
Cost example: 1M input and 1M output tokens
A cost example for 1M input plus 1M output tokens cannot be calculated reliably because separate numeric input and output rates are not listed for both models.
Choose DeepSeek-V3 if…
Choose DeepSeek-V3 when your task aligns with catalog-listed uses or strengths such as General-purpose coding and software development, Business document analysis, API building at high quality and low cost, Enterprise AI integration at scale.
Choose GPT-OSS 120B if…
Choose GPT-OSS 120B when your task aligns with catalog-listed uses or strengths such as Enterprise AI requiring OpenAI quality in open-weight form, Researchers wanting full access to OpenAI-quality model weights, 120B open parameters from OpenAI — frontier quality with open weights, MIT license — fully free commercial use.
Catalog specifications for DeepSeek-V3 and GPT-OSS 120B
Specification
DeepSeek-V3
GPT-OSS 120B
Provider
DeepSeek
Microsoft / OpenAI
Parameters
671B total (37B active via MoE)
120B
Context window
128K tokens
128K tokens
Maximum output
Not listed
Not listed
Catalog pricing
MIT open source. DeepSeek API: $0.27/MTok input, $1.10/MTok output
MIT — free
Listed input/output cost per 1M tokens
input $0.27, output $1.1 per 1M tokens
Separate numeric input/output rates not listed
License
MIT
MIT
Coding support
Listed
Listed
Reasoning support
Not listed
Listed
Vision input
Not listed
Not listed
Multimodal
Not listed
Not listed
Open weights
Yes
Yes
Benchmark: MMLU
88.5%
85.0%
Benchmark: HumanEval
89.9%
88.0%
Figures are transcribed from LITHOVEX model records. Benchmark versions and evaluation setups may differ; pricing and specifications can change. Check each provider's current documentation before making a decision.
Frequently Asked Questions
What is different between DeepSeek-V3 and GPT-OSS 120B?
DeepSeek-V3: 128K tokens; GPT-OSS 120B: 128K tokens. The listed context-window values are equal. DeepSeek-V3 is listed for coding, agentic workflows, long-context tasks; GPT-OSS 120B is listed for coding, reasoning, agentic workflows, long-context tasks. Shared catalog benchmark entries: MMLU: DeepSeek-V3 88.5%; GPT-OSS 120B 85.0%; HumanEval: DeepSeek-V3 89.9%; GPT-OSS 120B 88.0%. These records are reference data, not a controlled head-to-head test; verify current pricing and specifications with each provider.
What benchmark values are listed for DeepSeek-V3 and GPT-OSS 120B?
MMLU: DeepSeek-V3 88.5%; GPT-OSS 120B 85.0%; HumanEval: DeepSeek-V3 89.9%; GPT-OSS 120B 88.0%. The catalog values may use different versions and evaluation setups, so they are reference points rather than a controlled head-to-head result.
Which model has the larger listed context window?
DeepSeek-V3: 128K tokens; GPT-OSS 120B: 128K tokens. The listed context-window values are equal.
What are the listed costs per million tokens for DeepSeek-V3 and GPT-OSS 120B?
DeepSeek-V3: input $0.27, output $1.1 per 1M tokens (MIT open source. DeepSeek API: $0.27/MTok input, $1.10/MTok output). GPT-OSS 120B: Separate numeric input/output rates not listed (MIT — free). Where separate numeric input and output rates are available, the worked example uses 1M input plus 1M output tokens. Confirm current terms with each provider.
How far apart are the listed release dates for DeepSeek-V3 and GPT-OSS 120B?
The catalog lists DeepSeek-V3 as December 2024 and GPT-OSS 120B as 2025; a release-date gap cannot be calculated from both records.
When was the comparison data last updated?
The model catalog records 2026-10-16 as its data update date. Provider specifications and prices may have changed since then.
How should I choose between DeepSeek-V3 and GPT-OSS 120B?
Compare the listed specifications and capabilities, review the benchmark notes, then test both with the same representative prompt for your task. This page does not claim a universal winner.