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).

We evaluated Meta’s new Muse Spark 1.1 across our benchmark suite.

• It debuts at #4 on the Vals Index (68.41%), narrowly ahead of GPT 5.5 (67.95%). At $0.50/test, it is the lowest-cost model in the top 10; it also averaged 388.5 seconds on the index, roughly 3× faster than each of the top three models.

• It takes #2 on Finance Agent v2 (57.21%), just 0.65 points behind Gemini 3.5 Flash.

• The largest generational gain is on Vibe Code Bench. Compared with Muse Spark, Muse Spark 1.1 rises from #43 to #5 and improves by 52.48 points (19.67% → 72.16%).

• Muse Spark 1.1 sets new highs on domain-specific work: #1 on MedScribe (88.89%), #1 on TaxEval v2 (79.72%), and #1 on Harvey’s Legal Agent Benchmark (20.00%). On MedScribe, it is nearly twice as fast as the #2 model.

The model has a 1M-token context window and supports up to 256k output tokens. Evaluations were run with reasoning effort set to “xhigh” and default temperature and top-p.

Congrats to the Meta team on the release!