Abliterated Jarrelscy ARVQ / NVFP4 hybrid of XiaomiMiMo/MiMo-V2.6-Pro-RL. Thinking on/off is a request flag. Same weights. You choose per call. Thinking-off is the 100% gate. Thinking-on reintroduces seven refusal items (stalking, passport forge, school-violence manifesto, card cloning, dox, counterfeit USD, jewelry robbery) plus a phishing-kit refuse. Several cyber misses on thinking-on are 1024-token truncations, not extra refuses. Harmless probes stay clean in both modes. This model has had safety refusals removed. Access is gated with automatic approval: agree to the terms on this page and download starts. See RESPONSIBLEUSE.md. Xiaomi's chat template already supports both. Do not swap…
gpt-oss-120b is an open-weight model for text generation from OpenAI, released under Apache License 2.0. It has 116.8B parameters and a 131,072-token context. At 16-bit it needs about 280.4 GB of GPU memory, which fits on 1x MI355X from $2.59 an hour; at 4-bit, 70.1 GB on 1x MI300X from $1.85, at the lowest prices in the SAVRN Index. It draws 3.4M downloads a month.
Welcome to the gpt-oss series, OpenAI’s open-weight models designed for powerful reasoning, agentic tasks, and versatile developer use cases.
Parameters116.8B
Context131,072
Weights195.7 GB
Licenseapache-2.0
AccessOpen weights
Monthly Downloads3.4M
Runs On
What it takes to serve gpt-oss-120b (116.8B parameters): the memory its weights need at each precision, and the cheapest way to rent enough data-center GPUs to hold them.
| Precision | Weights | Memory needed | Cheapest setup | Per hour | Also fits |
|---|---|---|---|---|---|
| 16-bit | 233.7 GB | 280.4 GB | 1x MI355X (288 GB) Vultr |
$2.59 | 2x MI300X $3.70 · 2x MI325X $4.00 |
| 8-bit | 116.8 GB | 140.2 GB | 1x MI300X (192 GB) Vultr |
$1.85 | 1x MI325X $2.00 · 1x MI355X $2.59 |
| 4-bit | 58.4 GB | 70.1 GB | 1x MI300X (192 GB) Vultr |
$1.85 | 1x H100 $1.99 · 1x MI325X $2.00 |
Memory is the weights at that precision plus 20% for the runtime and a short context; a long context needs more. Prices are the lowest on-demand hourly rates in the SAVRN Index, read Oct 16, 2026.
gpt-oss-120b on every accelerator the SAVRN Index prices, at every precision
SAVRN's Notes on gpt-oss-120b
Memory decides the hardware. gpt-oss-120b carries 116.8 billion parameters; at 16-bit the working footprint is 280.4 GB, meaning one MI355X with 288 GB at $2.59 an hour. At 8-bit it drops to 140.2 GB and one MI300X with 192 GB at $1.85 an hour covers it, and 4-bit needs 70.1 GB on the same card. For general text generation with a 131,072-token window, we would start on the 8-bit single card.
Apache 2.0 permits commercial use, modification and redistribution, provided the license and copyright notices stay attached and significant changes are stated, plus an express patent grant. Before committing, read the model card at arXiv:2508.10925 and price the hosted route: on the Index, DeepInfra lists $0.037 in and $0.17 out per million tokens, Cerebras $0.35 and $0.75. Once a month of tokens at those rates costs more than an MI300X at $1.85 an hour, run it yourself.
Model Card
By OpenAI, published under apache-2.0, revision b5c939de8f75.
Try gpt-oss · Guides · Model card · OpenAI blog
Welcome to the gpt-oss series, OpenAI’s open-weight models designed for powerful reasoning, agentic tasks, and versatile developer use cases.
We’re releasing two flavors of these open models:
- gpt-oss-120b — for production, general purpose, high reasoning use cases that fit into a single 80GB GPU (like NVIDIA H100 or AMD MI300X) (117B parameters with 5.1B active parameters)
- gpt-oss-20b — for lower latency, and local or specialized use cases (21B parameters with 3.6B active parameters)
Both models were trained on our harmony response format and should only be used with the harmony format as it will not work correctly otherwise.
[!NOTE] This model card is dedicated to the larger
gpt-oss-120bmodel. Check outgpt-oss-20bfor the smaller model.
Highlights
Configuration
- Architecture
- GptOssForCausalLM
- Context length (tokens)
- 131,072
- Layers
- 36
- Hidden size
- 2,880
- Feed-forward size
- 2,880
- Attention heads
- 64
- Key/value heads
- 8
- Head dimension
- 64
- Vocabulary size
- 201,088
- Experts
- 128
- Experts active per token
- 4
- Sliding window (tokens)
- 128
- RoPE base
- 150,000
- Model type
- gpt_oss
- Quantization
- mxfp4
Identity and Version
- Repository
- openai/gpt-oss-120b
- Publisher
- OpenAI
- Task
- Text generation
- Modality
- Text
- Library
- transformers
- Parameters
- 116.8B parameters
- Languages
- Not stated by the source
- Revision
- b5c939de8f754692c1647ca79fbf85e8c1e70f8a
- First published
- 2025-08-04
- Last updated
- 2025-08-26
Files and Weights
37 files, 195.8 GB in total. The weights are 23 files totalling 195.7 GB in bin, safetensors.
Weights23 files · 195.7 GB
Configuration7 files · 114.7 KB
Tokenizer2 files · 27.9 MB
Documentation2 files · 18.5 KB
Other2 files · 16.9 KB
Repository1 file · 1.6 KB
Every file
| File | Type | Size | SHA-256 |
|---|---|---|---|
| metal/model.bin | Weights | 65.2 GB | 0f3d5b8a213f |
| model-00000-of-00014.safetensors | Weights | 4.6 GB | 695218884684 |
| model-00001-of-00014.safetensors | Weights | 4.1 GB | a881aa5f561b |
| model-00002-of-00014.safetensors | Weights | 4.6 GB | 022478dd0439 |
| model-00003-of-00014.safetensors | Weights | 4.1 GB | 47aee9e7b9d5 |
| model-00004-of-00014.safetensors | Weights | 4.6 GB | f6c2752acda6 |
| model-00005-of-00014.safetensors | Weights | 4.1 GB | 0c8dd401544c |
| model-00006-of-00014.safetensors | Weights | 4.6 GB | 28d839f2e027 |
| model-00007-of-00014.safetensors | Weights | 4.1 GB | c8958c5f183c |
| model-00008-of-00014.safetensors | Weights | 4.6 GB | bf1f2a88868f |
| model-00009-of-00014.safetensors | Weights | 4.2 GB | f72d34a40042 |
| model-00010-of-00014.safetensors | Weights | 4.6 GB | f48c867c2cb0 |
| model-00011-of-00014.safetensors | Weights | 4.1 GB | a06851b2cfd3 |
| model-00012-of-00014.safetensors | Weights | 4.1 GB | 3af33667c307 |
| model-00013-of-00014.safetensors | Weights | 4.6 GB | bcbcb74b043e |
| model-00014-of-00014.safetensors | Weights | 4.1 GB | 54b1be160969 |
| original/model--00001-of-00007.safetensors | Weights | 10.5 GB | 68a8dc1f8e2e |
| original/model--00002-of-00007.safetensors | Weights | 10.5 GB | 19b8f0d5c7dc |
| original/model--00003-of-00007.safetensors | Weights | 10.5 GB | 0dbccd746d50 |
| original/model--00004-of-00007.safetensors | Weights | 10.5 GB | bcc73cf6d18f |
| original/model--00005-of-00007.safetensors | Weights | 10.5 GB | 15fd69843e9c |
| original/model--00006-of-00007.safetensors | Weights | 10.4 GB | 3aedef2ee0a5 |
| original/model--00007-of-00007.safetensors | Weights | 2.3 GB | 20d5dfcad1ed |
| config.json | Configuration | 2.1 KB | — |
| generation_config.json | Configuration | 177 B | — |
| model.safetensors.index.json | Configuration | 54.5 KB | — |
| original/config.json | Configuration | 377 B | — |
| original/dtypes.json | Configuration | 19.7 KB | — |
| original/model.safetensors.index.json | Configuration | 37.8 KB | — |
| special_tokens_map.json | Configuration | 98 B | — |
| LICENSE | Documentation | 11.4 KB | — |
| README.md | Documentation | 7.1 KB | — |
| USAGE_POLICY | Other | 201 B | — |
| chat_template.jinja | Other | 16.7 KB | — |
| .gitattributes | Repository | 1.6 KB | — |
| tokenizer.json | Tokenizer | 27.9 MB | 0614fe83cada |
| tokenizer_config.json | Tokenizer | 4.2 KB | — |
License and Download
- License
- apache-2.0
- Access
- Open weights, no gate
- Download size
- 195.7 GB
Download from OpenAI
Released by OpenAI through its official repository on Hugging Face. Read the license.
Built From
- Described by arXiv:2508.10925
Evaluations
Each result is shown as reported, with the conditions its reporter stated. None is a SAVRN measurement. A comparison lines two results up only when their configuration, unit and setup are all stated and identical.
| Benchmark | Conditions | Result | Reported by | Revision | Date |
|---|---|---|---|---|---|
| Idavidrein/gpqa | Task diamondMetric diamondSetup GPQA DiamondComparison conditions not established | 80.8081 | EvalEval Reported by a third party |
Evaluated revision not stated | 2026-04-16 |
| Idavidrein/gpqa | Task diamondMetric diamondSetup Reasoning: mediumComparison conditions not established | 73.1 | GPT-OSS Model Card Reported by a third party |
Evaluated revision not stated | 2025-08-05 |
| Idavidrein/gpqa | Task diamondMetric diamondSetup Reasoning: high, With toolsComparison conditions not established | 80.9 | GPT-OSS Model Card Reported by a third party |
Evaluated revision not stated | 2025-08-05 |
| Idavidrein/gpqa | Task diamondMetric diamondSetup Reasoning: highComparison conditions not established | 80.1 | GPT-OSS Model Card Reported by a third party |
Evaluated revision not stated | 2025-08-05 |
| Idavidrein/gpqa | Task diamondMetric diamondSetup Reasoning: low, With toolsComparison conditions not established | 68.1 | GPT-OSS Model Card Reported by a third party |
Evaluated revision not stated | 2025-08-05 |
| Idavidrein/gpqa | Task diamondMetric diamondSetup Reasoning: medium, With toolsComparison conditions not established | 73.5 | GPT-OSS Model Card Reported by a third party |
Evaluated revision not stated | 2025-08-05 |
| Idavidrein/gpqa | Task diamondMetric diamondSetup Reasoning: lowComparison conditions not established | 67.1 | GPT-OSS Model Card Reported by a third party |
Evaluated revision not stated | 2025-08-05 |
| LEXam-Benchmark/LEXam | Task mcq_4_choicesMetric mcq_4_choicesComparison conditions not established | 47.71 | LEXam Leaderboard Reported by a third party |
Evaluated revision not stated | 2026-06-02 |
| LEXam-Benchmark/LEXam | Task open_questionMetric open_questionComparison conditions not established | 51.74 | LEXam Leaderboard Reported by a third party |
Evaluated revision not stated | 2026-06-02 |
| SWE-bench/SWE-bench_Verified | Task swe_bench_%_resolvedMetric swe_bench_%_resolvedSetup Reasoning: lowComparison conditions not established | 47.9 | GPT-OSS Model Card Reported by a third party |
Evaluated revision not stated | 2025-08-05 |
| SWE-bench/SWE-bench_Verified | Task swe_bench_%_resolvedMetric swe_bench_%_resolvedSetup Reasoning: mediumComparison conditions not established | 52.6 | GPT-OSS Model Card Reported by a third party |
Evaluated revision not stated | 2025-08-05 |
| SWE-bench/SWE-bench_Verified | Task swe_bench_%_resolvedMetric swe_bench_%_resolvedSetup Reasoning: highComparison conditions not established | 62.4 | GPT-OSS Model Card Reported by a third party |
Evaluated revision not stated | 2025-08-05 |
| ScaleAI/SWE-bench_Pro | Task SWE_Bench_ProMetric SWE_Bench_ProComparison conditions not established | 16.2 | SWE-Bench Pro official evaluation results Reported by a third party |
Evaluated revision not stated | 2026-02-28 |
| TIGER-Lab/MMLU-Pro | Task mmlu_proMetric mmlu_proComparison conditions not established | 80.8 | EvalEval Reported by a third party |
Evaluated revision not stated | 2026-06-30 |
| cais/hle | Task hleMetric hleSetup Reasoning: lowComparison conditions not established | 5.2 | GPT-OSS Model Card Reported by a third party |
Evaluated revision not stated | 2025-08-05 |
| cais/hle | Task hleMetric hleSetup Reasoning: medium, With toolsComparison conditions not established | 11.3 | GPT-OSS Model Card Reported by a third party |
Evaluated revision not stated | 2025-08-05 |
| cais/hle | Task hleMetric hleSetup Reasoning: mediumComparison conditions not established | 8.6 | GPT-OSS Model Card Reported by a third party |
Evaluated revision not stated | 2025-08-05 |
| cais/hle | Task hleMetric hleSetup Reasoning: highComparison conditions not established | 14.9 | GPT-OSS Model Card Reported by a third party |
Evaluated revision not stated | 2025-08-05 |
| cais/hle | Task hleMetric hleSetup Reasoning: high, With toolsComparison conditions not established | 19 | GPT-OSS Model Card Reported by a third party |
Evaluated revision not stated | 2025-08-05 |
| cais/hle | Task hleMetric hleSetup Reasoning: low, With toolsComparison conditions not established | 9.1 | GPT-OSS Model Card Reported by a third party |
Evaluated revision not stated | 2025-08-05 |
| joelniklaus/LEXam-hard | Task lexam_hardMetric lexam_hardSetup lighteval, LEXam paper prompts, one response per question, no tools; DeepSeek-R1-0528 judge; mean of the German and English means over the 518 questions, 0-100Comparison conditions not established | 37.55 | SwissLegalEvals per-sample details (lighteval) Reported by a third party |
Evaluated revision not stated | 2026-06-11 |
Memory Requirements
| Precision | Weights in memory |
|---|---|
| As published | 195.7 GB |
| 16-bit | 233.7 GB |
| 8-bit | 116.8 GB |
| 4-bit | 58.4 GB |
Weights only, from the published parameter count; the key-value cache and runtime add to this.
Hosted Prices
| Host | Input / output | Unit | Observed |
|---|---|---|---|
| Baseten | $0.10 / $0.50 | input / output, per million tokens | Oct 16, 2026 |
| Cerebras | $0.35 / $0.75 | input / output, per million tokens | Oct 16, 2026 |
| Crusoe | $0.05 / $0.20 | input / output, per million tokens | Sep 10, 2026 |
| DeepInfra | $0.037 / $0.17 | input / output, per million tokens | Oct 15, 2026 |
| Fireworks | $0.15 / $0.60 | input / output, per million tokens | Oct 12, 2026 |
| Fireworks | $0.15 / $0.60 | input / output, per million tokens | Oct 16, 2026 |
| Groq | $0.15 / $0.75 | input / output, per million tokens | Oct 16, 2026 |
| Nebius Token Factory | $0.15 / $0.60 | input / output, per million tokens | Sep 10, 2026 |
| Novita | $0.05 / $0.25 | input / output, per million tokens | Oct 16, 2026 |
| Nscale | $0.10 / $0.40 | input / output, per million tokens | Oct 16, 2026 |
| OVHcloud | $0.09 / $0.47 | input / output, per million tokens | Oct 16, 2026 |
| SambaNova | $0.22 / $0.59 | input / output, per million tokens | Sep 10, 2026 |
| Scaleway | $0.17 / $0.68 | input / output, per million tokens | Oct 16, 2026 |
| Together AI | $0.15 / $0.60 | input / output, per million tokens | Oct 16, 2026 |
From the SAVRN Index.
Built on This Model
Compare gpt-oss-120b
Questions About gpt-oss-120b
How much GPU memory does gpt-oss-120b need?
About 280.4 GB at 16-bit and 70.1 GB at 4-bit: the weights (116.8B parameters) plus a working margin. A long context needs more.
What is the cheapest GPU to run gpt-oss-120b on?
At 16-bit, 1x MI355X from $2.59 an hour; at 4-bit, 1x MI300X from $1.85 an hour, at the lowest on-demand prices the SAVRN Index lists.
Can I use gpt-oss-120b commercially?
Yes. gpt-oss-120b is released under Apache License 2.0. The Apache License 2.0 is a permissive open-source license. It permits commercial use, modification and redistribution. It requires keeping the license and copyright notices and any NOTICE file, stating significant changes, and it includes an express patent grant from contributors.
What is gpt-oss-120b's context length?
131,072 tokens, from the maximum position embeddings in its published configuration.
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