Join @brendanfoody and Harvey's @gabepereyra for the debut episode of The Frontier podcast. Learn about: - Frontier and open source AI - Post-training models for professional work - Future of AI and legal work Check out the episode below.

Claude Opus 5.5 is the new #1 on APEX-SWE. Overall, Opus 5.5 scores 67.6% Pass@1, a +3.9 point gain over the previous leader, Opus 5, at 63.7%. APEX-SWE measures observability, debugging from production telemetry, and integration, if a model can build an end-to-end system. Compared with Opus 5: Observability Opus 5.5: 69.8% (#1) Opus 5: 63.5% (#2) It gains +6.3 points. Opus 5.5 leads the board by 6.3 over Opus 5 and 10.8 over Fable 5.1. Integration Opus 5.5: 65.5% (#4) Opus 5: 64.0% (#9) Opus 5.5 improved at reading telemetry and finding faults, but building systems from scratch moved less. Congrats to @AnthropicAI. APEX-SWE leaderboard: mercor.com/apex/apex-swe-lea…

Mercor is committing $5M to a new AI Capabilities Fund. In conversations with researchers and enterprises, one challenge that comes up repeatedly is that many AI evals do not reflect how models are used. But to provide useful signal for eval and training, benchmarks need to capture actual workflows in representative environments. This will only become more difficult as models are used for more high-value, complex tasks. The Capabilities Fund supports researchers working on frontier eval challenges across a range of domains and data shapes, including: - Reward calibration and aligning grades with expert preferences - Creating high-quality realistic environments - Long-horizon workflows - Ambiguous requests and under-specified outcomes - Navigating social, temporal, and business context We will fund researcher time, API credits, and travel. We will also cover the cost to work with our network of 5M+ experts, as well as free use of our evals and analysis platform. Grants are available to independent researchers, non-profits, and academics. To submit an Expression of Interest, go to: mercor.com/careers/?ashby_ji…

OpenAI GPT‑6 Sol and Luna are on the APEX leaderboards. GPT-6 Sol scores 54.3% Pass@1 (#18) on APEX-Agents. That is +2.9 pts over GPT-5.6 Sol. GPT-6 Luna scores 44.3% Pass@1 (#29), +1.3 pts over GPT-5.6 Luna. On APEX-SWE, Luna scores 38.8% Pass@1, +6.7 pts. OpenAI reduced API prices for Sol and Luna by 50% compared with their GPT‑5.6 promotional pricing. GPT-6 Sol improves in finance and consulting domains: Investment banking: 55.2% Pass@1 (#13), +5.5 pts over GPT-5.6 Sol Management consulting: 51.6% (#16), +9.0 pts GPT-6 Sol is only 0.7 pts behind GPT-6 Astra in investment banking. These gains are not consistent across all domains. Compared with GPT-5.6, Sol’s corporate law score drops 5.7 pts to 56.2% (#27). Luna drops 2.8 pts to 55.6%. GPT-6 Luna improves most on coding work: On APEX-SWE, GPT-6 Luna scores 38.8% Pass@1, gaining +6.7 pts over GPT-5.6. Integration: 59.3%, +13.3 pts Observability: 18.3%, no change Sol scores 45.0% Pass@1 (#18), about level with GPT-5.6 Sol (45.8%). Integration: 62.3% (#13), level with GPT-6 Astra (62.0%) Observability: 27.8% (#19), down 3.7 pts For both models, debugging with production telemetry is still the hardest part. Congrats to the @OpenAI team on the launch. See full leaderboard: mercor.com/apex/apex-agents-…

Mercor has now partnered with > 300 enterprises to help them monetize their data, paying companies up to $10M per enterprise. Across these companies, we’ve encountered 90 different systems: -55% contained Slack -53% GitHub -53% Google Drive -50% Gmail -40% Figma -35% Jira A company with 200 employees runs on many of the same systems as an enterprise with 5,000+. For AI to do economically valuable work, it needs to understand how work actually happens across these systems — not just within any one of them.

APEX is now on the Specialized Intelligence Index, @FireworksAI_HQ's new leaderboard for real-work benchmarks by industry. We built APEX to test frontier models’ ability to do professional work. We’re proud to partner with Fireworks to make these benchmarks more widely available. APEX General Practitioner (MD) APEX-Agents Corporate Law APEX-SWE Find out which models lead each category. See the full results on SII: fireworks.ai/specialized-int…

Claude Opus 5.5 is the new #1 on APEX-Agents and APEX-Accounting. APEX-Agents: 73.5% Pass@1 (#1) 81.3% mean score (#1) APEX-Accounting: 15.4% Pass@1 (#1) 62.0% mean score (#1) On APEX-Agents, the new model gains +4.9 pp over Fable 5.1 (68.6%), the previous leader, and +7.6 pp over Opus 5 (65.8%). Anthropic says the biggest gains in this release are on long-running agentic tasks and knowledge work. That is what APEX-Agents measures, and the numbers agree. Opus 5.5 on APEX domains: Management Consulting: 80.0% Pass@1 (#1) Corporate Law: 71.2% Pass@1 (#3) Investment Banking: 69.3% Pass@1 (#2) Accounting: 62.0% mean score (#1) Token use can indicate domains where the model is most effective. Consulting leads at 80% and uses the fewest tokens at 2.0M per attempt. Law has the highest partial credit (85.7% mean) but costs the most at 4.9M tokens. In Accounting, Opus 5.5 gets partial credit on most tasks but fully passes only 1 in 6. More tokens can increase capability on Opus 5.5. Max effort consumes 3.50M tokens per attempt. That’s 2.1x more than Opus 5 and 1.2x Fable 5.1. But list price fell to $4/$20 per M (Opus 5 was $5/$25), and cache reads are $0.20, so 2.1x the tokens is only about 1.7x the dollars. Effort level has a significant impact on benchmark scores and token usage. Medium effort: 52.3% on 756k tokens Max effort: 73.4% on 3.50M tokens Increasing effort to max gains 21 points for 4.6x the tokens on agentic work. On APEX-Accounting, the same jump buys 2.8 points for 3.4x the tokens. When Opus 5.5 solves a task, it solves it consistently. 142 of 239 tasks passed on all 4 runs. Failing runs used 3.1M tokens vs 1.9M for passing runs, and took twice as long. 13 runs hit a hard failure, all in Corporate Law. 8 of them burned 25M to 42M tokens before dying, showing that long-horizon legal work can still send the model into a loop. Congratulations to the @claudeai team. See full leaderboard: mercor.com/apex/apex-agents-…

Don’t miss newly announced Fireworks Forge speaker: @BrendanFoody, CEO and Co-founder of @Mercor. Join the people, teams, and companies building their own frontier on open models. Nov 3, San Francisco. Apply to attend: fireworks.ai/forge#apply

I’ve been getting lots of DMs asking me how to pass Mercor.com assessments. 👀 Well… Mercor just made things a lot easier. They’ve released Mercor Academy — a learning platform where you can build and certify skills that are relevant to the work available on Mercor. You can now take courses, learn the skills you need, and earn certifications that appear on your Mercor profile. 🎓 One of the courses currently available is “Introduction to Agents”, where you learn what AI agents are, the components behind them, and how they work. If you’re trying to get into AI training, data work, or other opportunities on Mercor, this is definitely something worth checking out. Instead of just applying and hoping you pass the assessment, you can actually build the skills first. Mercor is basically giving you the roadmap. 🗺️ Go to your Mercor account → Learn → Academy and see what courses are available.

We’ve just added three new speakers to our incredible Fireworks Forge lineup! We are proud to welcome @BrendanFoody @pirroh and @jefftangney to the stage as we bring together the people, teams, and companies building their own frontier on open models.

@gabepereyra is the President & Co-Founder of @harvey, the most prominent legal AI startup and an industry leader in owning their own intelligence. I'm excited to share our conversation, which spans closed & open source, post-training, and how the law profession will evolve.

People often ask me what books and podcasts I recommend for learning about AI. The truth is, I learn most about the future of AI and the data market through conversations with leading AI researchers. I want to make more of those conversations available to everyone, so I’m starting a podcast interviewing the people shaping the frontier of AI. More soon 👀

Harvey is leading the wave of app-layer startups post-training models for their domain, in the same way Cursor did in code. @asadovsky is one of the best post-training leaders I've worked with across MAI and GDM. I couldn't be more excited for what this team accomplishes.

At this important juncture, building better evaluations across a diverse set of domains is the most impactful way to align models. We need subject matter experts across cyber, biology, chemistry, and many other industries to build benchmarks that help pace the frontier.