2026 turned reasoning into a product category. Six months ago, thinking meant a single chain-of-thought toggle. Today every major lab ships a dedicated reasoning tier that plans, self-checks, and works through hard problems before it answers.
This guide compares the new thinking models added to AI Crucible in mid-2026. Each one spends extra compute on internal reasoning tokens. That trade buys accuracy on math, code, analysis, and multi-step agent work, at a higher price and slower response.
Models covered: GPT-5.5, GPT-5.5 Pro, Claude Opus 4.8, GLM-5.1, DeepSeek-V4-Pro, Kimi K2.6, and Grok 4.3.
Time to read: 7-9 minutes.
A thinking model generates hidden reasoning tokens before its final answer. It drafts a plan, tests intermediate steps, and revises its own mistakes. You pay for those reasoning tokens as output, so one reply can cost several times more than a direct answer.
The payoff shows up on hard tasks. Reasoning models win on competition math, multi-file refactors, long proofs, and agent workflows that span many tool calls. For simple lookups or short rewrites, that extra thinking is wasted money and latency.
Prices below are each provider's published API rate per million tokens.
| Model | Provider | Context | Input | Output | Reasoning style |
|---|---|---|---|---|---|
| GPT-5.5 | OpenAI | 1.05M | $5.00 | $30.00 | Adaptive effort |
| GPT-5.5 Pro | OpenAI | 1.05M | $30.00 | $180.00 | Highest compute |
| Claude Opus 4.8 | Anthropic | 1M | $5.00 | $25.00 | Adaptive thinking |
| Grok 4.3 | xAI | 1M | $1.25 | $2.50 | Toggle reasoning |
| Kimi K2.6 | Moonshot | 256K | $0.95 | $4.00 | Deep, multimodal |
| GLM-5.1 | Z.AI | 200K | $1.40 | $4.40 | Long-horizon agent |
| DeepSeek-V4-Pro | DeepSeek | 1M | $0.435 | $0.87 | Dual think mode |
Two patterns stand out. First, output prices now span more than 200-fold, from $0.87 to $180 per million tokens. Second, the cheapest models match the expensive ones on many benchmarks, so price no longer tracks quality cleanly.
GPT-5.5 sets a reasoning effort from none to extra-high, so one model covers quick replies and deep analysis. It carries a 1.05M context window and 128K max output. GPT-5.5 Pro pushes effort further for the hardest problems. Pro can take minutes per answer, so a background job suits it best.
Opus 4.8 is Anthropic's most capable model for coding and long agent runs. It blends adaptive thinking with a 1M context window at $5 input and $25 output. Strong instruction-following and tool use make it a safe default for complex builds.
Grok 4.3 runs in both reasoning and fast modes, so you tune depth per request. It pairs a 1M context window with low pricing at $1.25 input and $2.50 output. That combination makes it a strong value pick for mixed workloads.
Kimi K2.6 is a native multimodal model with deep reasoning across text, images, and video. It offers a 256K context window at $0.95 input and $4.00 output. Cached input drops to $0.16, which rewards repeated context.
GLM-5.1 targets agent tasks that run for hours across many steps. It uses interleaved reasoning and a 200K context window at $1.40 input and $4.40 output. Open weights also make it attractive for self-hosting.
DeepSeek-V4-Pro switches between thinking and non-thinking modes in one model. It supports a 1M context window and 384K max output at $0.435 input and $0.87 output. For the price, its reasoning quality is hard to beat.
Match the model to the cost of a wrong answer. High-stakes reasoning justifies premium models; routine work does not.
Always test on your own prompts. Benchmark wins rarely transfer cleanly to your specific domain and data.
AI Crucible runs several models on one prompt, then has them critique and refine each other. Thinking models suit this setup because they expose stronger intermediate reasoning for peers to check.
A common pattern pairs a premium reasoner with a cheaper one. The expensive model leads on hard sub-problems, while the cheaper model challenges its logic and controls cost. Strategies like Debate Tournament and Expert Panel make that collaboration explicit.
No. Reasoning tokens add latency and price, so they hurt on simple tasks. Use a fast model for lookups, short rewrites, and classification. Reserve thinking models for problems where a wrong answer is expensive to fix.
For everyday speed and price, see the companion guide on the fastest AI models of 2026.