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Coding and Security Benchmarks

Evaluations such as FrontierSWE, the Artificial Analysis Intelligence Index, and vulnerability-detection tests used to compare open and closed models.

Current stories

investOne report1 publisher

MIT and Sakana AI's SIFT finishes a coding agent's self-improvement search on about $34 of API calls

MIT and Sakana AI's SIFT ran a coding agent's full self-improvement search on roughly $34 of API calls, about a tenth of the Darwin Godel Machine's resources. The saving comes from a language-model judge screening patches, so it holds only while that judge picks correctly.

Reality

Evidence40
Adoption
Not scored
Hype gap+25
Incentives
Not scored
Confidence45
buildConfirmed3 publishers

Four passing runs out of 80 separate first from second on Specific's private-code benchmark

Specific Labs scores coding agents on licensed production codebases. The best setup clears 38.8%. The analysis covers ten tasks at eight runs each, so every published score is a count of passing rollouts out of 80.

Perspective Coverage

3 publishers
Builder
Builder 52%
Operator
Operator 33%
Investor
Investor 15%

Reality

Evidence48
Adoption
Not scored
Hype gap+30
Incentives70
Confidence58
productOne report1 publisher

Antigravity gives away five frontier models on a quota Google keeps trimming

Antigravity's free tier includes Gemini 3.1 Pro and Claude Opus 4.6 with unlimited tab completions. XDA reports Opus burns credits about four times faster than Gemini, and Google moved the free tier onto credits in March 2026.

Reality

Evidence36
Adoption
Not scored
Hype gap+12
Incentives58
Confidence40
buildOne report1 publisher

JetBrains ships a 50/50 weight average of two Qwen checkpoints in Junie Local

The merged 27B finished 37 of 100 internal coding tasks against 34 for the fast checkpoint and 39 for the reasoning one, and it did that while emitting 71% fewer output tokens than the 39, with no post-training in the recipe.

Reality

Evidence55
Adoption30
Hype gap+18
Incentives78
Confidence58
buildOne report1 publisher

Alibaba's Open Code Review hands file picking and line positioning back to deterministic code

Alibaba ran the reviewer internally for two years across tens of thousands of developers before releasing it. The write-up is specific about how files get selected and comments get positioned, and it stops short of publishing the benchmark scores.

Publishers:dev.to

Reality

Evidence32
Adoption30
Hype gap+55
Incentives68
Confidence38
buildOne report1 publisher

JetBrains' Kotlin leaderboard prices the same 86 solved tasks at 3.7x apart

The Kotlin Benchmark grades agents on 105 verified repository tasks. Its token column shows setups that solve within a few tasks of each other burning between 66,000 and 777,000 tokens per fix.

Publishers:dev.to

Reality

Evidence62
Adoption30
Hype gap+22
Incentives60
Confidence58
buildOne report1 publisher

A refactoring benchmark stops the best agent at 41.2%, and the tests are the story

SWE-Bench ProMax puts frontier coding agents on 170 curated refactoring commits. The number that should move procurement is a different one: nearly 60% of unsolved SWE-bench Verified tasks have flawed tests.

Publishers:thenewstack.io

Reality

Evidence38
Adoption14
Hype gap+12
Incentives62
Confidence42
leadershipOne report1 publisher

A Government Switched Off Two Frontier Models. Your Board Will Want The Fallback Plan.

A June 13 Commerce Department order cut non-US users off from two Anthropic models overnight, and OpenAI followed with limits of its own. Model access is now a sovereign risk, not a vendor term.

Publishers:forbes.com

Reality

Evidence22
Adoption20
Hype gap+45
Incentives68
Confidence30