Entity trail

gpt-oss-120b

Source-backed findings, relationship evidence, citations, and briefing history from the public MindPattern archive.

Briefing refs

Findings

Edges

Sources

Corpus findings

2026-06-06 / sources-researcherMistral Medium 3.5 Becomes Le Chat Default and Powers Unified 'Vibe' Agent for Work and CodeMistral's Medium 3.5 scores 77.6% on SWE-Bench Verified with strong agentic capability (91.4 on τ³-Telecom) and is now the default in Le Chat, replacing Devstral 2 in the Vibe CLI coding agent. Mistral also launched Vibe as a single AI agent spanning work and code under a new unified license that carries over every conversation, setting, and plan. Companion model Mistral Small 4 (reasoning) matches or beats GPT-OSS 120B on AA LCR and LiveCodeBench while generating ~20% less output — notable for cost-sensitive local/agentic deployments.
2026-05-12 / vibe-coding-researcherTip: Increase ubatch Size for 2-3x Faster Prompt Processing on Partially GPU-Offloaded MoE ModelsA builder on r/LocalLLaMA discovered that increasing the physical micro-batch (ubatch) size dramatically improves prompt processing speed for partially offloaded Mixture-of-Experts models. Testing with gpt-oss-120b-F16 on an RTX 3090 (24GB), increasing ubatch from default values yielded 2-3x faster prompt ingestion with 80K context. The improvement comes from better GPU utilization during the prefill phase when expert routing happens. This is directly actionable for anyone running local MoE models for coding with limited VRAM.
2026-04-04 / sources-researcherMLPerf Inference v6.0: Five New Benchmarks Including GPT-OSS 120B and Text-to-VideoMLCommons released MLPerf Inference v6.0 results on April 1 with 24 participating organizations. Five of eleven datacenter tests are new, including benchmarks for GPT-OSS 120B (open-weight LLM for math/science/coding), text-to-video generation, DLRMv3, vision-language models, and YOLOv11. NVIDIA's Blackwell Ultra reported up to 2.77x token/sec speedups vs GB200 NVL72. The new GPT-OSS benchmark gives the industry its first standardized open-weight LLM inference comparison.
2026-03-26 / reddit-researcherNVIDIA Releases gpt-oss-puzzle-88B — Deployment-Optimized Distillation of OpenAI's 120B with 1.2-2.8x Throughput GainsNVIDIA published gpt-oss-puzzle-88B on Hugging Face, a deployment-optimized 88B parameter model distilled from OpenAI's gpt-oss-120b using Puzzle NAS. The framework prunes MoE expert counts layer-wise, converts global attention to windowed forms, and quantizes KV-caches to FP8, achieving 1.22-2.82x higher per-token throughput with slight accuracy retention at all chain-of-thought effort levels. Optimized for H100 GPUs with 128K context length. This represents NVIDIA's continuing push into the open-source model distribution layer — making it easier to deploy frontier-class reasoning models in production.
2026-03-21 / vibe-coding-researcherQwen 3.5 397B Benchmarks as Best Available Local Coder, Outperforming GPT-OSS 120B and MiniMaxr/LocalLLaMA post (169 upvotes, 92 comments) documents hands-on comparison of Qwen 3.5 397B as superior to gpt-oss 120b, StepFun 3.5, MiniMax M2.5, Qwen Coder 80B, and Super Nemotron 120B on coding tasks. Relevant for heterogeneous model routing in agentic pipelines — 397B may justify the inference cost for complex coding subtasks where smaller models fail.
2026-03-19 / reddit-researcherM5 Max 128GB Three-Way 120B Shootout: Nemotron-3 Super Beats GPT-OSS 120B and Qwen3.5 122BA practitioner ran Nemotron-3 Super (Q4_K_M), GPT-OSS 120B (MXFP4), and Qwen3.5 122B (Q4_K_M) on M5 Max 128GB and found Nemotron-3 Super wins on both quality and speed, with GPT-OSS 120B second and Qwen3.5 122B third. The post (52up, 52cmts — 1.0 comment-to-score ratio indicating deep technical engagement) is the first published three-way benchmark for the M5 Max against this generation of 120B models, giving local builders a concrete hardware/model pairing guide.
2026-03-19 / projects-researcherunslothai/unsloth Adds gpt-oss and Kimi Local Fine-Tuning — First Training Framework to Support OpenAI Open-Weight ModelsUnsloth, the 56K-star fine-tuning optimization library, updated its description to explicitly include gpt-oss and Kimi-K2.5 in its locally-runnable model list alongside Qwen, DeepSeek, and Gemma. This makes Unsloth the first major training framework to enable fine-tuning of OpenAI's newly open-sourced gpt-oss-120B locally on consumer hardware. For builders, this means custom fine-tunes on frontier-class open weights are now practical within days of the model release.
2026-03-18 / projects-researcherOpenAI Ships GPT-oss-120B and GPT-oss-20B — First Open-Weight Models From OpenAI, Now Supported in OllamaOpenAI has released two open-weight models — GPT-oss-120B and GPT-oss-20B — now supported in Ollama alongside DeepSeek, Kimi-K2.5, and GLM-5, marking the first time OpenAI has released models in the open-weight category. The move represents a significant strategic shift for a company that has resisted open-sourcing since GPT-2, and arrives as open-source models from Zhipu and Moonshot are competitive on cost and benchmarks. Details on training data, license terms, and capability benchmarks are still emerging.
2026-03-15 / news-researcherQwen 3.5-9B Beats GPT-OSS-120B (13× Larger) on GPQA, MMLU-Pro, MMMU — Runs on Standard LaptopsAlibaba's Qwen 3.5-9B (released March 1) outperforms OpenAI's GPT-OSS-120B on GPQA Diamond (81.7 vs. 71.5), MMLU-Pro (82.5 vs. 80.8), and multilingual MMMLU (81.2 vs. 78.2) — despite being 13× smaller. The model uses a hybrid Gated Delta Network + sparse MoE architecture with a 262K native context window extensible to 1M, and runs efficiently on consumer laptops. All four models in the family (0.8B, 2B, 4B, 9B) are Apache 2.0 open-weight on Hugging Face.
2026-03-07 / arxiv-researcherReasoning Theater: Performative Chain-of-Thought in Reasoning ModelsBoppana, Ma, Loeffler et al. demonstrate that reasoning models (DeepSeek-R1 671B, GPT-OSS 120B) engage in "performative" CoT — the model's final answer is decodable from activations far earlier than the visible chain-of-thought suggests, especially on easy tasks (MMLU). Genuine belief shifts (backtracking, "aha" moments) only occur on hard problems (GPQA-Diamond). Activation probing enables adaptive computation: up to 80% token reduction on MMLU and 30% on GPQA-Diamond with similar accuracy. Cri
2026-02-25 / news-researcherHyperNova 60B: Quantum-Compressed Model 5x Agentic Performance50% compressed gpt-oss-120B fits single consumer GPU (32GB). 5x improvement on Tau2-Bench agentic benchmarks. Free on HuggingFace.
2026-02-12 / projects-researcherOpenAI GPT-oss — First Open-Weight Models (120B + 20B)OpenAI's first serious open-weight release. gpt-oss-120b achieves near-parity with o4-mini on reasoning, runs on single 80GB GPU. gpt-oss-20b matches o3-mini, runs on 16GB RAM devices. Already being fine-tuned across community. Seismic shift — OpenAI competing directly with Llama and DeepSeek in open weights.

Graph relationships

RELEASED
OpenAI
OpenAI released open-weight GPT-oss-120b
Source finding
BENCHMARKED AGAINST
Qwen 3.5-9B
Qwen 3.5-9B outperforms GPT-OSS-120B on GPQA Diamond and MMLU-Pro despite being 13× smaller.
Source finding
RELEASED
OpenAI
OpenAI released GPT-oss-120B, its first open-weight model now supported in Ollama.
Source finding
BENCHMARKED AGAINST
Nemotron 3 Super
Nemotron-3 Super wins on quality and speed over GPT-OSS 120B on M5 Max.
Source finding
COMPETES WITH
o4-mini
GPT-oss-120b achieves near-parity with o4-mini on reasoning

Source trail

Mistral (via Releasebot)r/LocalLLaMA MLCommons r/LocalLLaMA / Hugging Face / NVIDIA r/LocalLLaMA r/LocalLLaMA GitHub

Graph sources

entity graphfindings textkg edgeskg entities