• We evaluated Alibaba’s new Qwen 3.7 Max across our benchmark suite. The model places #9 on the Vals Index, scoring 51.64%.

• On SWE-bench Verified, Qwen 3.7 Max scores 68.6% on our Vals Index subset, a strong result that is competitive with Claude Opus 4.7 (68.5% on the same subset). This is a notable achievement for a model from a Chinese provider.

• On CorpFin v2, the model scores 65.4% on shared max context, below its predecessor Qwen 3 Max Thinking (68.0%). Finance Agent v2 results are mid-tier at 41.9%.

• On coding benchmarks, Qwen 3.7 Max scores 42.7% on Terminal Bench 2.0 — a regression from Qwen 3.6 Max Preview (51.7%). Vibe Code Bench comes in at 41.5%, and ProofBench at 26.0%.

The model has a ~1M-token context window. Evaluations were run with temperature 0.7 and preserve_thinking enabled.

• We evaluated Google’s new Gemini 3.5 Flash across our benchmark suite. It is the new #1 model on our Finance Agent Benchmark v2, dethroning GPT 5.5 by 6 points.

• The model places #3 on the Vals Index, scoring 62.05%, and #3 on the Vals Multimodal Index, scoring 62.29%. That is an ~8-point increase over Gemini 3.1 Pro Preview (02/26) on the Vals Index and a ~7-point increase on the Vals Multimodal Index.

• We also saw a meaningful increase on Vibe Code Bench, our benchmark measuring how well models can create web applications from scratch. Gemini 3.5 Flash ranked #10, scoring 48.68% — a 16-point jump relative to Gemini 3.1 Pro Preview (02/26). The model also did well on other coding benchmarks, ranking #3 on SWE-bench Verified and #3 on Terminal Bench 2.0.

• On MedCode, our benchmark that assesses whether models can support the medical billing process, Gemini 3.5 Flash ranks #3 overall — just 3.2 points behind the top model, Gemini 3.1 Pro Preview (02/26). It also had a slight 3-point increase on ProofBench.

• The model still lags behind Gemini 3.1 Pro Preview (02/26) on some of our benchmarks: LegalBench, MortgageTax, and academic benchmarks including MMLU Pro, GPQA.

The model has a 1M-token context window. Evaluations were run using a reasoning effort of “high”, a temperature of 1.0, and max output tokens set to 65k.

Congrats to the Google team on the strong release!

We’ve released v1.1 of the Vals Index and Vals Multimodal Index.

The main change is the finance component: both indices now use the Finance Agent v2 index subset, scored as the mean of three runs per model. The rest of each index is unchanged.

GPT 5.5 is the new leader on both updated indices, scoring 67.62% on Vals Index v1.1 and 67.77% on Vals Multimodal Index v1.1.

Claude Opus 4.7 is close behind on both leaderboards, with Claude Sonnet 4.6 rounding out the top three.

We’ve released Finance Agent v2, the next generation of our benchmark for evaluating AI agents on realistic financial analyst tasks.

• Nine analytical categories spanning retrieval, market analysis, comparables, precedents, adjustments, earnings, disclosure, and financial modeling.
• Severity-weighted partial credit with dealbreaker gating: a question receives no credit when a load-bearing fact is wrong, regardless of how much of the surrounding answer is correct.
• Every model is evaluated three times on the full benchmark; reported scores are the mean across runs.

Compared to v1.1, scores fall ~14 points on average across models evaluated on both versions, with the steepest drop in Financial Modeling — where multi-step arithmetic and finance-specific conventions are least forgiving under the new rubric.

GPT 5.5, Claude Opus 4.7, and Claude Sonnet 4.6 are the top three performers, separated by less than a point.