We evaluated Google’s Gemini 3.6 Flash on the Vals Index and an initial set of proprietary benchmarks.

• Gemini 3.6 Flash scores 62.43% on the Vals Index, placing #14 of 38 models overall and finishing within 0.32 points of Gemini 3.5 Flash.

• Its coding results include 77.45% on the SWE-bench Verified Vals Index subset and 57.88% on the Vibe Code Bench Vals Index subset, improvements of 1.96 and 3.17 points over Gemini 3.5 Flash, respectively.

• Gemini 3.6 Flash scores 73.78% across three full trials of Terminal-Bench 2.1, placing #8 of 43 models. It also places #7 of 74 models on MedCode, scoring 53.15%.

• On MedScribe, the model scores 79.66%, a 3.09-point increase over Gemini 3.5 Flash.

We evaluated Gemini 3.6 Flash with temperature 1, high reasoning effort, and up to 65k output tokens. The model supports a 1M-token context window, multimodal inputs, and tool calling.

Additional full-benchmark results will be added as they complete.

Congrats to the Google team on the release!

We evaluated Moonshot AI’s Kimi K3 on the Vals Index.

• Kimi K3 scores 74.70% on the Vals Index, placing #2 of 38 models overall.

• Its strongest component results are 95.10% on the SWE-bench Verified Vals Index subset and 91.27% on the Vibe Code Bench Vals Index subset.

• Kimi K3 scores 80.90% across three full trials of Terminal-Bench 2.1, 72.61% on CorpFin v2, and 55.88% on the Finance Agent v2 Index subset.

We evaluated Kimi K3 with temperature 1, up to 262k output tokens, and max-effort reasoning. Kimi K3 supports a 1M-token context window, image inputs, and tool calling.

Congrats to the Moonshot AI team on the release!

We evaluated Thinking Machines’ first open-weights model, Inkling, on the Vals Index.

• Inkling scores 49.28% on the Vals Index, placing #25 of 37 models overall and #8 among open-weight models.

• Its strongest component results include 75.49% on the SWE-bench Verified Vals Index subset and 69.23% on the CorpFin v2 Vals Index subset. It also scores 45.97% on the Finance Agent v2 Index subset.

• Inkling scores 13.28% on the Vibe Code Bench Vals Index subset and 47.57% across three full trials of Terminal-Bench 2.1.

We used Thinking Machines’ recommended evaluation settings: temperature 1, top-p 1, up to 256k output tokens, separate reasoning enabled, and reasoning effort set to "0.99". Inkling supports a 256k context window, image inputs, and tool calling.

Congrats to the Thinking Machines team on the release!

We evaluated OpenAI’s new GPT-5.6 Sol and GPT-5.6 Terra across our benchmark suite.

• Sol ranks #2 on the Vals Index (72.63%) and the Vals Multimodal Index (72.19%).

• Agentic coding is the headline: Sol takes #1 on SWE-bench Verified (96.20%) and Terminal-Bench 2.1 (85.77%). It also scores 80.50% (#4) on Vibe Code Bench and takes #1 on ProofBench (77.00%).

• Sol also leads CyberBench (88.14%), the Excel Modeling Benchmark (72.34%), and Legal Research Bench (48.08%).

• Terra scores 65.14% on the Vals Index and 65.07% on the Vals Multimodal Index, with strong results on ProofBench (71.00%, #3), Legal Research Bench (40.87%, #4), and Terminal-Bench 2.1 (73.41%, #7).