The prediction market for top coding AI models saw a dramatic shift on October 28, 2026. Contracts for Anthropic’s "Claude" jumped from 1% to 99% probability on the Kalshi exchange. This move followed GitHub’s Universe conference, where the company introduced Copilot Fusion. The new tool scored within two points of Opus 5.5 Max, effectively endorsing the model as the industry standard. Traders priced in this apparent lead, causing contracts for rivals like ChatGPT and Dola to collapse by over 90 percentage points each.
Benchmarking the Reasoning Gap
Epoch AI’s composite index places GPT-6 Astra (Max) at 169.1, slightly ahead of Claude Opus 5.5 at 168.4. However, the Artificial Analysis Coding Agent Index released on October 22 showed "Claude Code with Opus 5.5" achieving a leading score of 61.7. Multiple independent analyses ranked Claude Code as the top tool for complex engineering tasks. Developers note that o1-preview often outperforms Claude 3.5 Sonnet in debugging intricate logic and maintaining context over long conversations. In one comparison, o1-mini hit 89.5% on AIME 2024, while Claude 3.5 Sonnet managed only 16.0% without tools. Yet, Claude 3.5 Sonnet tops SWE-bench Verified at 49.0%, a metric where o1 struggles to match consistency.
| Model | Context Window | Input Price ($/1M) | Output Price ($/1M) |
|---|---|---|---|
| Claude 3.5 Sonnet | 200K | $3.00 | $15.00 |
| GPT-o1 mini | 128K | $0.00315 | $0.0126 |
| Claude Opus 5.5 | N/A | $2.50 (Batch) | $12.50 (Batch) |
o1 spends more time reasoning internally, which improves accuracy on hard math problems but increases latency. Users report waiting up to 30 seconds for responses, making it less suitable for rapid iteration. Claude 3.5 Sonnet processes outputs at 80 tokens per second, compared to o1 mini’s 23. This speed advantage allows developers to iterate faster on simple tasks. The cost difference is stark; o1 charges $60.00 per million output tokens, while Claude Haiku 4.5 costs just $5.00. For high-volume workloads, the price gap alone makes o1 an expensive choice.
Practical Trade-offs and Costs
Anthropic’s newer tokenizer produces approximately 30% more tokens for the same text than earlier models. This means the real saving on Sonnet 5 is closer to 13%, not the 33% suggested by sticker prices. You should compare vendors on measured cost per completed task, not rate cards. Run the same 20 representative requests through each candidate and compare invoices. Reasoning tokens bill as output tokens, even though you never see them. A model thinking for 4,000 tokens before emitting a 200-token answer bills roughly 4,200 output tokens. On GPT-5.6 Sol at $20.00 output, that single call costs about $0.08.
GitHub’s endorsement of Claude Opus 5.5 signals a consolidation of developer trust. The LMArena Code leaderboard remains the official settlement source, but traders are front-running its update. If the leaderboard reflects the benchmark results, the 99% probability will be validated. One question remains: will the lag between new benchmarks and official rankings persist? Microsoft detailed its adoption of the underlying GitHub-built harness on the same day as the announcement. The data suggests Claude holds the edge in coding utility, while o1 retains a niche for deep scientific reasoning. Choose based on your specific workload complexity and budget constraints.
