Entity trail

gpt-oss-20b

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.
2026-06-04 / skill-finderCut GRPO RL fine-tuning VRAM 50–90% with Unsloth to train reasoning models on a single mid-tier GPUUnsloth reduces VRAM for GRPO reinforcement-learning fine-tuning by 50–90% versus standard Flash-Attention-2 setups, enabling gpt-oss-20b GRPO training in 15GB VRAM (free on Colab) and 1.2–1.7× longer context with no slowdown. GRPO uses group-based reward comparison as an on-the-fly baseline, eliminating the separate memory-hungry value model.
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.
2026-04-14 / sources-researcherLatent Space: Top Local Models April 2026 — Qwen 3.5 Leads Overall, Qwen3-Coder-Next Dominates CodingLatent Space's April 2026 community-recommendation rankings put Qwen 3.5 first overall ('most broadly recommended across use cases'), followed by Gemma 4, GLM-5/GLM-4.7, MiniMax M2.7/M2.5, DeepSeek V3.2, and GPT-oss 20B. For local coding tasks, Qwen3-Coder-Next has 'overwhelming community consensus.' Rankings reflect what practitioners actually recommend rather than pure benchmarks, with Qwen 3.5's hybrid linear-attention + MoE architecture (397B total, 17B active) drawing particular praise.
2026-04-12 / hn-researcherSmall Models Also Found the Vulnerabilities That Mythos Found — 1,113 Points on HN, #1 StoryAISLE founder Stanislav Fort published a counter-analysis showing that models as small as 3.6B parameters (GPT-OSS-20b at $0.11/Mtok) reproduced Mythos's cybersecurity findings, including a FreeBSD NFS exploit and a 27-year-old OpenBSD SACK bug. Small models actually outperformed frontier models on an OWASP false-positive test, correctly tracing Java data flow where Claude and GPT flagships failed. The conclusion: 'the moat in AI cybersecurity is the system, not the model' — capability doesn't scale smoothly with model size.
2026-04-10 / vibe-coding-researcherAISLE Research: Small Open-Weight LLMs Match Mythos on Vulnerability Detection — System Beats ModelAISLE tested Anthropic's Mythos-discovered vulnerabilities against 8 smaller open-weight models. All 8 — including GPT-OSS-20b at just 3.6B active params ($0.11/M tokens) — successfully detected the FreeBSD NFS exploit. On OWASP false-positive data flow tracing, DeepSeek R1 outperformed frontier models from Anthropic, OpenAI, and Google. The researchers conclude 'the moat in AI cybersecurity is the system, not the model' — orchestration and validation frameworks matter more than raw model capability.
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.
2026-03-27 / hn-researcherChroma Context-1: Open-Weight 20B Search Agent Achieves Frontier-Level Retrieval at 10x Speed and 25x Lower CostChroma released Context-1, a 20B-parameter agentic search model trained via SFT and RL from gpt-oss-20B. It decomposes queries into subqueries, searches iteratively, and self-edits its context window by pruning irrelevant documents (0.94 prune accuracy). Competitive with frontier models on BrowseComp-Plus, SealQA, FRAMES, and HLE benchmarks while being 10x faster and 25x cheaper. John Schulman endorsed the approach. Apache 2.0 licensed, weights on Hugging Face.
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.

Graph relationships

RELEASED
OpenAI
OpenAI released open-weight GPT-oss-20b
Source finding
CRITICIZES
GRP-Obliteration
GRP-Obliteration jailbreak attack increases success rate against GPT-OSS-20B from 13% to 93%.
Source finding
RELEASED
OpenAI
OpenAI released GPT-oss-20B, its first open-weight models marking a strategic shift.
Source finding
BENCHMARKED AGAINST
Claude
Small models like GPT-OSS-20b reproduced cybersecurity findings previously attributed to larger models.
Source finding
BENCHMARKED AGAINST
Context-1
Context-1 was trained via SFT and RL from gpt-oss-20B.

Source trail

Mistral (via Releasebot)Unsloth Documentation — Memory Efficient RL r/LocalLLaMA Latent Space AISLE MLCommons Chroma / Hacker News

Graph sources

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