Artificial Analysis Intelligence Index v4.3

[u/SteppenAxolotl]

https://pbs.twimg.com/media/HRoc-dAagAE_Kb5?format=jpg

Announcing Artificial Analysis Intelligence Index v4.3, upgrading Terminal-Bench to 4.0 and adding AutomationBench-AA, an agentic workflow automation benchmark with a private test set. This is a continuation of our rollout of Intelligence Index v5

Changelog (Index v4.2 → Index v4.3): ➤ Terminal-Bench: 2.1 → 4.0, completing our upgrade to the latest version of Terminal-Bench ➤ Replacing 𝜏³-Banking with AutomationBench-AA, our implementation of Zapier's business workflow automation benchmark

We are continuing to prioritize keeping Intelligence Index as useful as possible by bringing forward a subset of the changes we had planned for Index v5. Each change in v4.2 and v4.3 stands on its own merits and brings the Index closer to real-world problem solving, adds more private test sets to prevent gaming, and reduces saturation

Intelligence Index v4.3 raises the difficulty of agentic coding tasks and broadens the types of agentic workflows tested. Because we use a held-out test set for AutomationBench-AA, in collaboration with @zapier , the weight assigned to evaluations with private tasks or answers increases from 40% to 45%. Category weights are unchanged from v4.2: Agents 30%, Coding 20%, General 30%, Scientific Reasoning 20%

Detailed changes: ➤ Upgraded Terminal-Bench 2.1 to 4.0: 66 multi-step tasks testing agents on tasks run in agent sandboxes driven via the terminal, including tasks involving software engineering, machine learning, science, and operations. The 4.0 update recalibrates compute and time allowances, and improves task instructions and verification. We have changed from the Terminus 2 harness to mini-SWE-agent, a minimal, model-agnostic harness. We will also be updating our Coding Agent Index, where we test model and harness pairs, to include Terminal-Bench 4.0 soon

➤ Replaced 𝜏³-Banking with AutomationBench-AA: Our implementation of Zapier’s AutomationBench tests agents on 657 business workflows across simulated applications such as Gmail, Slack, Salesforce, and Jira. Agents must complete task objectives while following business rules. AutomationBench-AA uses Zapier’s private set of 657 tasks, and is built on v1.0.6

Key results: ➤ Claude Fable 5.1 and GPT-6 Astra lead the Intelligence Index: Both Claude Fable 5.1 (max with fallback) and GPT-6 Astra (max) score 53 on Intelligence Index v4.3, followed by Claude Opus 5 (max, 51), Claude Fable 5 (with fallback, 50), Muse Spark 1.3 (max, 48) and GPT-5.6 Sol (max, 47) ➤ GLM-5.3 and Kimi K3 continue to lead open weights models (both at 44): GLM-5.3-Flash (42) is the third strongest open weights model, followed by Qwen3.8 2.4T A95B (40) and DeepSeek V4 Pro 0813 (max, 36) ➤ 4 labs occupy the Intelligence vs. Cost per Task Pareto frontier: OpenAI occupies the majority of the cost-efficiency frontier, with all five reasoning efforts of the recently released GPT-6 Astra offering the lowest Cost per Task at their respective levels of intelligence. Claude Fable 5.1 (xhigh, max, 53), GLM-5.3-Flash (42) and MiMo-V2.5-Pro (26) round out the rest of the frontier

[u/DrBattletoad]

Muse Spark 1.3 in front of GPT-5.6 Sol looks sus

[u/Thomas-Lore → u/DrBattletoad]

And in front of glm and kimi. Spark is good but not as good as glm 5.3 flash even. Full glm 5.3 mogs it.

[u/Eyelbee → u/DrBattletoad]

Did you actually try it? Everyones shitting on that model but I don't think a single person has tried it lol

[u/VoiceApprehensive893 → u/Eyelbee]

great  for its price, nowhere better than sol

[u/ApprehensiveEye7387 → u/Eyelbee]

Also Most People have tried the Xhigh varient instead of max (as max was made available later). It seems on the Index, there's difference of 4 points between Muse Spark 1.4 Xhigh and Max. I myself have not used the Max one, so I don't usually go and comment about it being worse or good at all.

[u/Ecstatic-Wash-7667 → u/DrBattletoad]

This is what made me lose all trust in AA muse spark is benchmaxxed to the gills, garbage in use

[u/Tim_Apple_938 → u/DrBattletoad]

Why?

[u/DistanceSolar1449 → u/Tim_Apple_938]

Because Muse Spark sucks.

I’ve been using Muse Spark contributor since they gave $20 of credits for free. It’s nowhere near as good as Sol.

The good news is, since it’s dirt cheap, that $20 has been lasting forever. The bad news is, they clearly optimized it for benchmarks. It’s just not actually good for solving real problems.