Entity trail

MRCR

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

Briefing refs

Findings

Edges

Sources

Corpus findings

2026-05-08 / sources-researcherGPT-5.5 Long-Context Breakthrough: 74% on MRCR v2 at 512K-1M Tokens — 37-Point Jump Over GPT-5.4GPT-5.5's long-context performance represents its most dramatic improvement: 74.0% on MRCR v2 at 512K-1M token contexts, up from GPT-5.4's 36.6% — a 37-point jump. At 128K-256K tokens it scores 87.5% vs Claude's 59.2%. Combined with 72% fewer output tokens per task, this makes GPT-5.5 significantly cheaper for long-document agent workflows. For builders: if your agent pipeline processes large codebases or document collections, the cost-per-task improvement is substantial.
2026-05-03 / rss-researcherOpenAI Releases GPT-5.5 — Natively Omnimodal Architecture, 82.7% Terminal-Bench, 4M Weekly Codex UsersGPT-5.5 launched April 23-24 as OpenAI's first natively omnimodal model (text, images, audio, video in unified system). Leads Terminal-Bench 2.0 at 82.7% (+13 points over Claude Opus 4.7), scores 84.9% on GDPval (44 real occupations), and 74% on MRCR v2 at 1M tokens (doubling GPT-5.4). Priced at $5/$30 per million tokens. Codex powered by GPT-5.5 now reaches 4M developers weekly with 400K context window and 1.5x faster token generation.
2026-05-02 / hn-researcherClaude Opus 4.7 Backlash Reaches New Peak — MRCR Benchmark Collapse from 78.3% to 32.2%, 35% Token InflationCommunity backlash against Claude Opus 4.7 has intensified with quantified evidence of degradation: the MRCR long-context benchmark collapsed from 78.3% (Opus 4.6) to 32.2%, the new tokenizer consumes up to 35% more tokens for identical inputs, and Claude Code is flagging routine code as malware. AI Explained's analysis video is trending alongside a 216-upvote r/ClaudeAI thread. Anthropic's Claude Code head Boris Cherny confirmed that 'adaptive thinking' was shifted to 'medium effort' in early March, which users characterize as 'AI shrinkflation.'
2026-04-18 / reddit-researcherOpus 4.6 vs 4.7 MineBench Results — 582↑ as Minecraft-Based Benchmark Reveals Specific Capability TradeoffsA detailed MineBench comparison (582↑ on r/ClaudeAI, cross-posted to r/singularity at 454↑) used Minecraft as a benchmark environment to show where Opus 4.7 specifically gains and loses capabilities versus 4.6. The gallery post includes multiple comparison visualizations. This is a new benchmark dimension beyond the NYT Connections and MRCR regressions reported yesterday, providing practitioners with more granular data on which tasks to use which model for.
2026-04-17 / reddit-researcherOpus 4.7 Benchmark Regressions Quantified — MRCR 78.3%→32.2%, NYT Connections 94.7%→41.0%, Thematic Generalization 80.6→72.8Independent benchmarks confirm the community complaints are not perception bias. Multi-Round Context Retrieval (MRCR) collapsed from 78.3% to 32.2% — a 59% relative decline. The NYT Connections Extended benchmark (lechmazur/GitHub) dropped from 94.7% to 41.0% on Opus 4.7 (high). Thematic Generalization fell from 80.6 to 72.8. Anthropic's own BrowseComp regressed 84.0%→79.3%. The model wins 12 of 14 official benchmarks but the regressions cluster in reasoning and context tasks — exactly what power users rely on.
2026-03-19 / reddit-researcherClaude 1M Context Window Now GA: No Long-Context Premium, 78.3% MRCR v2, Rolling Out to CoWorkAnthropic confirmed general availability of the 1M context window for Opus 4.6 and Sonnet 4.6 at standard pricing — no multiplier, a 900K-token request billed at the same rate as a 9K one. Opus 4.6 scores 78.3% on MRCR v2 (highest among frontier models at that length), and media handling expanded 6x to 600 images or PDF pages per request. The r/ClaudeAI community flagged that Claude CoWork also received the 1M context upgrade (197up), making it practical for loading entire codebases or full multi-day agent traces.
2026-03-15 / sources-researcherAnthropic Drops Long-Context Surcharge: 1M Token Context Now at Standard Per-Token Pricing for Opus 4.6 and Sonnet 4.6Anthropic eliminated the prior 2x input / 1.5x output long-context pricing premium — a 900,000-token request now costs identical per-token to a 9,000-token one. Opus 4.6 is $5/$25 per million input/output; Sonnet 4.6 is $3/$15. MRCR v2 retrieval accuracy at 1M tokens is 78.3%, best among frontier models; also includes automatic server-side context compaction and a 6x increase in image/PDF per-request limit (600 pages). The pricing change materially improves ROI on long-document agent pipelines.

Graph relationships

BENCHMARKED AGAINST
Opus 4.7
Multi-Round Context Retrieval collapsed from 78.3 percent to 32.2 percent on Opus 4.7.
Source finding
BENCHMARKED AGAINST
Opus 4.7 -> MRCR
Multi-Round Context Retrieval collapsed from 78.3 percent to 32.2 percent on Opus 4.7.
Source finding

Source trail

OpenAI's latest OpenAI Zyphra top post on r/ClaudeAI Multi-Round Context Retrieval (MRCR) collapsed from 78.3% to 32.2%silently broke existing API integrations stealth tax on top of it

Graph sources

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