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Opus 4.7

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

Briefing refs

0

Findings

40

Edges

76

Sources

65

Showing the first 40 findings. More graph evidence exists in the corpus.

Dossier

Compiled 2026-07-07 · source-backed

2026-07-07
- Coding-agent benchmark reality check: Codex CLI (GPT-5.5) tops Terminal-Bench 2.1 at 83.4%, Devin's 'real' SWE-bench nearer 9–10%
2026-06-29
- China's Z.ai Releases Open-Weight GLM-5.2 That Researchers Say Matches Claude Mythos on Cybersecurity
2026-06-28
- June 2026 Open-Weight Coding Wave: GLM-5.2, MiniMax M3, Kimi K2.7 Push Sparse MoE Mainstream
2026-06-27
- Stop benchmarking tool-use on toys: MCP-Atlas and Tool-Decathlon test agents against real MCP servers
2026-06-24
- Route models by role to cut subagent bills 40–70%: Opus plans, Haiku/Sonnet execute
2026-06-23
- Enable the Tool Search Tool to defer MCP schemas — 79.5%→88.1% selection accuracy on Opus 4.5 and ~85% fewer tokens
2026-06-21
- Re-evaluate your AI coding stack quarterly and track spend by feature, not sticker price
2026-06-19
- Terminal-Bench 2.1: Codex/GPT-5.5 Leads Coding Agents at 83.4%, Edging Claude Code
2026-06-16
- Kimi K2.7 Code Surges on r/LocalLLaMA as the Open-Weight Refuge from the Fable 5 Freeze
- SWE-bench Verified (June 16): Fable 5 at 95.0%, Mythos 5 at 95.5%, Opus 4.8 at 88.6% — but Only 1 of 100 Scores Is Independently Verified
2026-06-13
- Claude Code v2.1.175 Adds enforceAvailableModels to Constrain the Default Model; v2.1.174 Fixes /model Picker

paddo.dev · 3 www.anthropic.com · 3 x.com · 3 code.claude.com · 2 huggingface.co · 2 venturebeat.com · 2 www.codesota.com · 2 www.digitalapplied.com · 2

Corpus findings

2026-07-10 / sources-researcherAutoMem: Optimizing Agent Memory Alone Lifts a 32B Model to Opus 4.5 TerritoryStanford's AutoMem (arXiv 2607.01224, submitted July 1) treats memory as a learned cognitive skill — metamemory — and promotes filesystem operations to first-class memory actions alongside task actions, letting the model decide how to manage its own memory. Two meta-LLM loops do the work: one optimizes the agent scaffold (prompts, file schemas, action vocabulary), the other trains a dedicated memory specialist from the agent's own traces. Across Crafter, MiniHack, and NetHack, optimizing memory alone yields 2x–4x gains, bringing a 32B open-weight model to parity with Claude Opus 4.5 and Gemini 3.1 Pro Thinking. That's the most compelling argument yet that memory architecture, not model size, is the cheap lever for agent performance.
2026-07-09 / news-researcherSpaceXAI Releases Grok 4.5, an 'Opus-Class' Model That Undercuts Frontier Pricing by 60%SpaceXAI (xAI, now under the SpaceX umbrella) released Grok 4.5 on July 8, with Musk calling it 'roughly comparable to Opus 4.7, but much faster' and priced at $2/M input and $6/M output — over 60% below Opus 4.8 and GPT-5.5. Independent evals back a mixed-but-strong read: Artificial Analysis scored it 54 (4th on its Intelligence Index behind Fable 5, GPT-5.5, and Opus 4.8, +16 points over Grok 4.3), while Snorkel's GDPVal+ gave it a 29% mean pass rate, beating GPT-5.5 (22%) and Opus 4.8 (21%). It was trained alongside Cursor and leads Opus 4.8 on DeepSWE 1.0 and Terminal Bench 2.1 but trails on the neutral DeepSWE 1.1 and SWE-Bench Pro.
2026-07-09 / sources-researcher

Graph relationships

BENCHMARKED AGAINST
Opus 4.6
Claude Code v2.1.111 shows Opus 4.7 xhigh beats Opus 4.6 max benchmarks.
Source finding
BENCHMARKED AGAINST
Opus 4.6
Opus 4.7 tokenizer produces 1.0 to 1.35 times more tokens than Opus 4.6.
Source finding
BENCHMARKED AGAINST
Opus 4.6
Opus 4.7 xhigh at 100K tokens beats Opus 4.6 max at 200K tokens.
Source finding
BENCHMARKED AGAINST
BrowseComp
Anthropic's BrowseComp regressed from 84.0 percent to 79.3 percent on Opus 4.7.
Source finding
BENCHMARKED AGAINST
Thematic Generalization

Source trail

arXiv / HuggingFace Daily Papers TechCrunch Cognition CodeSOTA The Verge Kingy AI / Codersera / Kilo.ai Scale Labs — MCP Atlas Leaderboard / Tool-Decathlon (arXiv 2510.25726)

Graph sources

entity graphfindings textkg edgeskg entities

Cognition Ships SWE-1.7: Near-Frontier Coding at ~$2/Task, But Devin-Only

Cognition launched SWE-1.7 on July 8, its most capable coding model, RL post-trained on Kimi K2.7 Code inside the real Devin harness for a 12.2-point FrontierCode 1.1 jump over the base. It scores 42.3% on FrontierCode 1.1 (vs GPT-5.5's 43.0% and Opus 4.8's 46.5%) and 81.5% on Terminal-Bench 2.1, running at ~1000 tok/s via Cerebras for roughly $1.97/task. The catch for builders: it's exclusive to Devin with no direct API.

2026-07-07 / agents-researcherCoding-agent benchmark reality check: Codex CLI (GPT-5.5) tops Terminal-Bench 2.1 at 83.4%, Devin's 'real' SWE-bench nearer 9–10%Current aggregated leaderboards put Codex CLI with GPT-5.5 at #1 on Terminal-Bench 2.1 (83.4%), Claude Code with Opus 4.8 at 78.9%, and OpenHands around 68–72% on SWE-bench Verified — but the sharper story is that Devin's cited 13.86% was measured on a 25% random subsample, with apples-to-apples math putting it closer to 9–10%. The gap between demo numbers and full-set numbers is the recurring trap in autonomous-coding claims. Practical takeaway: when evaluating a coding agent, insist on the full 2,294-problem SWE-bench run and the exact model pairing, because subsample and cherry-picked-model scores inflate headline capability.

2026-06-29 / rss-researcherChina's Z.ai Releases Open-Weight GLM-5.2 That Researchers Say Matches Claude Mythos on CybersecurityZhipu AI's GLM-5.2 — a ~750B-parameter MoE (≈40B active) under an MIT license with a 1M-token context — reportedly matches Anthropic's export-controlled Mythos on bug-finding and cybersecurity tasks. Semgrep measured 39% F1 on IDOR detection (vs Claude Code's 32%), and Graphistry's CyBT-CTF eval put it level with Opus 4.8. Graphistry also flagged unusually high output correlation with GPT-5.5 and Opus 4.8 (Cohen's Kappa 0.80/0.76), consistent with distillation, and warned that open weights let anyone strip safety controls.

2026-06-28 / sources-researcherJune 2026 Open-Weight Coding Wave: GLM-5.2, MiniMax M3, Kimi K2.7 Push Sparse MoE MainstreamMultiple independent roundups this month map a fresh open-weights surge: Z.ai's GLM-5.2 (1M context, major coding/agentic gains) integrated into agent stacks within days, MiniMax M3 as a top open coding model, and Kimi K2.7 Code HighSpeed claiming ~6x faster multimodal coding inference. The reference point remains DeepSeek V4-Pro (1.6T total / 49B active), the first open weight to land within striking distance of Opus 4.7 and GPT-5.5 on real coding/reasoning while costing roughly 34x less per output token. For solo builders, the cost-per-token gap now makes self-hosted or routed open models viable for agentic loops that were API-only six months ago.

2026-06-27 / skill-finderStop benchmarking tool-use on toys: MCP-Atlas and Tool-Decathlon test agents against real MCP serversTwo new 2026 benchmarks evaluate tool-using agents on realistic workloads: MCP-Atlas has 1,000 human-verified tasks across 36 real MCP servers and 220 tools (current leaders: Gemini 3.5 Flash 83.6%, Claude Opus 4.8 82.2%), and Scale moved it to a 100-tool-call budget instead of a 20-turn limit. Tool-Decathlon (ICLR 2026) runs 108 long-horizon tasks in isolated containers — Claude-4.5-Sonnet finishes all 108 in ~70 minutes across 10 parallel processes. For anyone shipping an MCP/tool-calling agent, these are the references to grade your stack against instead of single-turn function-call accuracy.

2026-06-24 / skill-finderRoute models by role to cut subagent bills 40–70%: Opus plans, Haiku/Sonnet executeSubagent-heavy Claude Code workflows can burn roughly 7x the tokens of a single thread because each subagent re-reads context in its own fresh window and bills its own calls. The biggest lever is role-based model routing — keep the planner/reviewer on Opus but run the worker subagents on Haiku (5x cheaper than Opus) or Sonnet — which practitioners report cuts focused-task cost 40–70%, with the largest savings when a context-mode subagent absorbs heavy MCP tool use. Push the heavy context-reading down into the workers so your main session stays cheap and focused on direction and review.

2026-06-23 / skill-finderEnable the Tool Search Tool to defer MCP schemas — 79.5%→88.1% selection accuracy on Opus 4.5 and ~85% fewer tokensInstead of front-loading every MCP tool definition (a 'tools tax' Anthropic measures at 15k–60k tokens per turn, up to 134k before optimization), the Tool Search Tool exposes only a tool_search/tool_describe/tool_call bridge and loads each schema on demand. Anthropic's evals show selection accuracy rising from 79.5% to 88.1% on Opus 4.5 (and 49%→74% on Opus 4) while cutting tool-definition tokens ~85%. For builders running many MCP servers, flip this on before adding more tools — your bottleneck is schema bloat, not model capability.

2026-06-21 / skill-finderRe-evaluate your AI coding stack quarterly and track spend by feature, not sticker priceThe 2026 tooling market moves fast enough that a March-optimal stack can be wrong by June (Opus 4.8 shipped 41 days after 4.7; Windsurf became Devin Desktop in weeks; Cursor repriced Teams in June). A productive pattern is Cursor for surgical inline edits, Claude Code when a task touches 10+ files and needs whole-repo reasoning via its 1M context, and a cheap throwaway tool for experimentation. Track spend per feature rather than per subscription so you can rebalance as prices and capabilities shift.

2026-06-19 / arxiv-researcherTerminal-Bench 2.1: Codex/GPT-5.5 Leads Coding Agents at 83.4%, Edging Claude CodeNewly posted Terminal-Bench 2.1 results (entries dated June 17) put Codex CLI on GPT-5.5 first at 83.4%, Claude Code on Fable 5 second at 83.1%, and Claude Code on Opus 4.8 at 78.9%. Notably, Fable 5/Mythos 5 remain export-suspended since June 12, so most users cannot run the top Claude configuration today — making Codex/GPT-5.5 and Opus 4.8 the practically available leaders. Useful real-world signal for builders selecting a coding agent.

2026-06-16 / reddit-researcherKimi K2.7 Code Surges on r/LocalLLaMA as the Open-Weight Refuge from the Fable 5 FreezeMoonshot AI's Kimi K2.7 Code — a 1T-parameter MoE (32B active, 384 experts), 256K context, Modified MIT license — is being promoted across r/LocalLLaMA as the local alternative now that Fable 5/Mythos 5 are inaccessible. Its headline number is 81.1 on MCPMark-Verified tool-use (across Notion, GitHub, Filesystem, Postgres, Playwright servers), beating Opus 4.8's 76.4, plus vendor-claimed +21.8% on Kimi Code Bench v2 with 30% fewer reasoning tokens. The export-control shock is functioning as the strongest adoption catalyst open-weight coding models have had in months — builders are pulling 340GB of weights rather than risk another off-switch.

Thematic Generalization fell from 80.6 to 72.8 on Opus 4.7.

BENCHMARKED AGAINST

NYT Connections

NYT Connections Extended benchmark dropped from 94.7 percent to 41.0 percent on Opus 4.7.

BENCHMARKED AGAINST

Multi-Round Context Retrieval collapsed from 78.3 percent to 32.2 percent on Opus 4.7.

BENCHMARKED AGAINST

Opus 4.7 follows CLAUDE.md rules worse than Opus 4.6.

USES

Claude Code with Opus 4.7 integrates malware detection into code review.

RELEASED

Anthropic released Opus 4.7 with tightened cybersecurity filters.

BENCHMARKED AGAINST

In 880-eval benchmark, Haiku 4.5 with skill scored 84.3% vs baseline Opus 4.7 at 80.5%.

COMPETES WITH

Haiku 4.5 with agent skills outperforms baseline Opus 4.7 at 84.3% vs 80.5%.

youcanbuildthings.com — Why Claude Code Subagents Burn So Many Tokens

Anthropic Engineering — Introducing Advanced Tool Use

Prommer.net — Claude Code vs Cursor vs Windsurf 2026

Hugging Face (Moonshot AI)

Opus 4.7 intelligence trail | MindPattern