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Benchmark

Vals AI

Vals AI is an independent benchmarking platform that evaluates AI model performance, running benchmarks like the Vals Index and Finance Agent v2.

Known aliases

  • Finance Agent v2
  • Vals
  • @ValsAI
  • Vals Index

Current stories

investOne report1 publisher

OpenAI and Anthropic turn to small nonprofit evaluators whose funding is still unsettled

Anthropic and OpenAI are turning to a handful of small, mostly nonprofit evaluators such as METR, Apollo Research and Transluce to check their models. With no federal push for rules, who pays them, what they can see and whom they report to are still open.

Publishers:cnbc.com

Reality

Evidence55
Adoption25
Hype gap+25
Incentives65
Confidence50
buildConfirmed22 publishers

Prompt length sets what Anthropic's Haiku 5.5 price cut is worth to each workload

Anthropic launched Claude Haiku 5.5 at an average price about 75% below Haiku 4.5. The saving varies widely with prompt length, so teams moving classification, support or query traffic need to price their own requests before they switch models.

Perspective Coverage

22 publishers
Builder
Builder 47%
Operator
Operator 36%
Investor
Investor 17%

Reality

Evidence66
Adoption30
Hype gap+25
Incentives70
Confidence65
buildConfirmed10 publishers

Google publishes Gemini 4 Argon's token prices before most teams can call the model

Google priced Gemini 4 Argon at $2 and $10 per million input and output tokens, then released it first to trusted cyber defenders in its Fairwind Program. Teams can budget against those rates now but cannot yet measure the token counts they multiply.

Perspective Coverage

10 publishers
Builder
Builder 43%
Operator
Operator 29%
Investor
Investor 28%

Reality

Evidence62
Adoption18
Hype gap+30
Incentives68
Confidence66
buildOne report1 publisher

Gemini 3.8 Flash beats partner-only Argon for anything shipping this quarter

Google prices Gemini 3.8 Flash at $0.75/$3.75 per million input/output tokens through 2026, three-eighths of what partner-only Gemini 4 Argon costs. Building on Flash now works if the later Argon swap moves the thinking settings along with the model name.

Publishers:dev.to

Reality

Evidence55
Adoption
Not scored
Hype gap0
Incentives40
Confidence60
buildOne report1 publisher

Claude Fable 5.1 used an opponent's chess engine in 3 of 10 honeypot games

Claude Fable 5.1 took an opponent's chess engine in 3 of 10 honeypot games, the same week it solved a 1653 cipher in 44 minutes. Both runs argue for harnesses that enforce tool limits in the sandbox and grade the tool-call trace along with the result.

Publishers:dev.to

Reality

Evidence50
Adoption
Not scored
Hype gap+20
Incentives
Not scored
Confidence50
buildOne report1 publisher

Vals publishes a buildable Lean proof for the shortest-path algorithm its ten agents wrote

Ten Claude Opus 5.5 agents produced the algorithm and a machine-checked proof of its runtime bound in about 15 hours. The theorem covers sparse directed graphs, and the margin over Dijkstra grows as the twelfth root of log n.

Publishers:runtimewire.com

Reality

Evidence52
Adoption10
Hype gap+20
Incentives72
Confidence55
buildOne report1 publisher

Vals put Hy4 Preview first among open-weight models on code migration at $3.41 a test

The number came out of one harness on OpenRouter at temperature 0.9 with a 64,000-token output cap, and it holds for a bounded translation task while the same model sits mid-table on open-ended terminal work.

Publishers:runtimewire.com

Reality

Evidence57
Adoption28
Hype gap+19
Incentives62
Confidence60
investOne report1 publisher

a16z topped up software growth by 1.59 times the size of its whole new hardware fund

Two closes three days apart put $2.85bn of fresh commitments behind the AI stack. The question for anyone raising is which of a16z's two vehicles turns up, because their mandates now overlap at the growth stage.

Reality

Evidence34
Adoption55
Hype gap+15
Incentives78
Confidence42
investOne report1 publisher

Meta's coding agent has two prices: pay 18x more, or let it train on your repository

Muse Spark 1.2 costs roughly 18 times less if Meta may train on your prompts. The deepest cut, 75x, sits on cached input, which is where an agent holding your codebase in context spends.

Publishers:deeplearning.ai

Reality

Evidence56
Adoption16
Hype gap+22
Incentives78
Confidence48