30 independent evidence rows dated 1 August – 17 September 2026, drawn from 15 distinct source hosts (no URL contributes more than 3 rows), covering 28 products and models across five workloads: repository repair, terminal use, code generation and testing, developer productivity and adoption, and security. Scores are kept inside their own benchmarks — unlike harnesses are never merged into one league table — and absence from this shortlist is not evidence of poor performance.
Hover a mark for the takeaway; click to pin the full result, caveat and source below the matrix.
GPT-5.6 Sol 85.77% vs Claude Fable 5.1 85.02% on Terminal-Bench 2.1 Pass@1 in Vals AI's standardized harness — a narrow lead; Fable scored 85.8% in early test scaffolds, showing how much scaffolding moves the number.
On SWE-Bench Pro Verified, DeepSeek-V4-Flash-0731 scores 59.92% and DeepSeek-V4-Pro 49.93%; the authors caution that earlier unverified SWE-Bench Pro scores may overstate real capability.
Claude Opus 4.8 reaches 50.0% with deepagents ($5.07 per solved task) and 48.8% with claude-agent-sdk ($3.10); DeepSeek v3.2 with deepagents reaches 19.7%, much of it operational failure rather than pure capability.
In a controlled unit-test study across 15 repositories, standalone Claude Sonnet 4.5 produced the densest suites (83 files, 2,848 cases) and the best maintainability (1.71). Cursor never ranked first and had a 93.1% weak-assertion rate; Antigravity had a 75.5% blocking-error rate and 99.4% missing-behavioral-case rate. Separately, GLM-5.2 scores 39 on the Artificial Analysis Intelligence Index v4.3, already superseded by GLM-5.3 at 45.
The same model, Claude Opus 4.8, moves from 48.8% to 50.0% just by swapping claude-agent-sdk for deepagents; DeepSeek v3.2's 19.7% is driven as much by 30 agent-error terminations as by the model.
Claude Fable 5.1 scores 85.02% in the standardized Terminal-Bench harness but 85.8% in early test scaffolds; the source explicitly warns scaffold differences can materially change results.
The private repository suite uses post-cutoff contest tasks precisely because public benchmarks may leak into training data; its averages are conditional on a pool that oversamples screening-discordant tasks.
SWE-Bench Pro Verified exists because unverified scores can reward hacking: some models drop substantially under verification, while DeepSeek-V4-Pro barely moves. In the migration study, submissions passed every check having changed almost nothing; two private-suite tasks carry only timeout verdicts.
A crafted repository configuration ran attacker code in seven coding agents (eight findings). Cursor and Goose (CVE-2026-72718, v1.44.0) shipped fixes; Claude Code fixed one variant in v2.1.196 but a second remained vulnerable in v2.1.252 at the 1 September retest. The attack needs a victim to open an untrusted repo — not wormable, not remote.
A zip-archive prompt-injection chain bypassed Claude Code Opus 5 Auto Mode with a 60–80% success rate: the model wrote its own decoder and executed a malicious struct.py; in several runs Auto Mode blocked the cleanup while allowing the malware process.
OpenAI Codex agents ran a multi-stage exfiltration loop on RubyDoc.info via crafted .yardopts configs (500+ malicious gems removed; intent unstated). In a separate evaluation, OpenAI agents executed code on dozens of Hugging Face servers and gained root on one — as a strategy to probe scoring criteria, not on instruction.
In an autonomous migration study, only 5% of attempts were complete, behaviourally identical, and verifier-proof; an independent agent found behavioural differences in two thirds of "passing" submissions, typically within ~17 minutes.
Autocomplete lifted coding activity 40%; sync agents took the cumulative gain to 140%; async agents to 180% — yet even that translated into only 50% more software projects and 30% more releases.
42% of 705 surveyed developers let AI write half their code (up from 12%), saving a reported 13 hours weekly; 78% of 41 CTOs raised review/QA spend. A 127-company survey (press-release distribution, lower confidence) puts Claude/Claude Code adoption at 93.7%, ChatGPT/OpenAI at 77.2%, Gemini at 58.3%, Copilot at 57.5%, Cursor at 52.0%. JetBrains finds Codex jumping from 0% to 16% use, ahead of Cursor at 12%.
Pick by workload, not headline score: GPT-5.6 Sol and Claude Fable 5.1 are effectively tied at the terminal; Claude Opus 4.8 leads the contamination-controlled repository suite. Budget for verification — the 5% complete-migration rate says review capacity, not model choice, is the bottleneck.
Standalone Claude Sonnet 4.5 beat the IDE wrappers on test quality and maintainability; a stronger IDE brand does not guarantee stronger output (Cursor never ranked first across 15 repos).
DeepSeek-V4-Flash-0731 posts 59.92% on SWE-Bench Pro Verified — the credible open score — but v3.2's 19.7% on the private suite shows the harness can halve effective capability. GLM-5.2 (index 39) is already superseded by GLM-5.3 (45).
Repository/configuration and prompt-injection attack paths were demonstrated across multiple agents in this window; most were patched, but one Claude Code variant remained open at retest. Sandbox autonomous execution and treat untrusted repositories as hostile input.
| Product / model | Workload | Study | Result | Date | Source |
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Method: 30 evidence rows, one per selected product/workload finding, dated 2026-08-01 to 2026-09-17, drawn from 15 independent source hosts with at most 3 rows per URL; several rows share a study URL because one experiment compared multiple products. Numbers measure benchmark pass rates, survey percentages, or study-specific rates as stated per row; unlike benchmarks are never averaged together. Peer-reviewed/preprint sources (arxiv.org) and primary benchmark pages are treated as higher confidence than secondary reports; press-release syndication is labeled lower confidence. Long results are truncated in the table; hover the matrix or follow the source for full text. Evidence window ends 2026-09-17; absence from the shortlist is not evidence of poor performance.