2025 marked a watershed moment in artificial intelligence: the emergence of true reasoning models. What started with OpenAI's o1 evolved into a full ecosystem of "thinking" models that don't just predict the next token — they reason through problems step by step. This guide covers the complete 2025-2026 reasoning model landscape and shows you how to leverage these powerful systems cost-effectively.
What Are Reasoning Models?
Reasoning models are AI systems that explicitly work through problems using chain-of-thought (CoT) reasoning before generating responses. Unlike standard models that produce answers in one forward pass, reasoning models:
- Generate intermediate reasoning steps — The model "thinks through" the problem, showing its work
- Self-verify solutions — They check their own logic before finalizing answers
- Allocate compute dynamically — Harder problems get more reasoning tokens
- Achieve dramatically better results — Especially on math, coding, and complex logic
The key difference: Standard models like GPT-4o are fast but surface-level. Reasoning models like o3 and DeepSeek R1 take longer but achieve genuinely new levels of capability on hard problems.
The 2025 Reasoning Revolution: Why o3 and R1 Changed Everything
Before 2025, AI reasoning was limited to prompting tricks — telling models to "think step by step." Then everything changed:
OpenAI o3: The Paradigm Change
Released in late 2024 and refined throughout 2025, o3 represented a genuine breakthrough. On the ARC-AGI benchmark, o3 achieved 87.5% — a jump from o1's 25% and far beyond what standard models could achieve. This wasn't incremental improvement; it was a phase transition in capability.
OpenAI then introduced GPT-5.2 Thinking, bringing reasoning capabilities to the GPT-5 family with chain-of-thought enabled by default for complex queries.
DeepSeek R1: The Open-Source Explosion
Chinese AI lab DeepSeek released R1 in January 2025, sending shockwaves through the industry. R1 matched o1's reasoning performance at a fraction of the cost and — crucially — released as open weight. Anyone could download, fine-tune, and deploy R1.
This sparked a cascade of Chinese reasoning models:
- Qwen3 Thinking — Alibaba's open-source reasoning model with strong multilingual capabilities
- Kimi K2.6 — Moonshot AI's reasoning model optimized for multi-agent systems
- Yi-Thread — 01.AI's reasoning-focused variant
Anthropic's Response: Claude 4.5
Anthropic integrated deep reasoning capabilities into Claude 4.5, marketed as achieving "true AI coding maturity." Rather than a separate "thinking" mode, Claude 4.5 incorporates reasoning natively, with extended thinking available for complex tasks.
Complete Reasoning Model Comparison 2026
All prices are per million tokens (input / output). Thinking/compute costs are shown separately:
| Model | Provider | Input / 1M | Output / 1M | Compute / 1M | Context | Best For |
|---|---|---|---|---|---|---|
| DeepSeek R1 | DeepSeek | $0.55 | $2.20 | Included | 64K | Budget reasoning |
| Qwen3 Thinking 32B | Alibaba | $0.50 | $2.00 | Included | 32K | Open-source local |
| o3-mini | OpenAI | $1.10 | $4.40 | Included | 200K | Cost-effective reasoning |
| Claude 4.5 Sonnet | Anthropic | $3.00 | $15.00 | $3.75 | 200K | Coding + reasoning |
| GPT-5.2 Thinking | OpenAI | $7.50 | $30.00 | $3.75 | 128K | Maximum capability |
| o3 (high) | OpenAI | $15.00 | $60.00 | Variable | 200K | Research-grade reasoning |
| Kimi K2.6 | Moonshot | $2.00 | $8.00 | Included | 128K | Multi-agent systems |
First-Generation Models (Still Relevant)
| Model | Provider | Input / 1M | Output / 1M | Context | Notes |
|---|---|---|---|---|---|
| o1 | OpenAI | $15.00 | $60.00 | 128K | Original reasoning model |
| o1-mini | OpenAI | $1.10 | $4.40 | 128K | Cheaper o1 variant |
When to Use Thinking Models vs Standard Models
Not every task needs reasoning. Here's when each approach wins:
Use Thinking/Reasoning Models For:
- Complex multi-step math — o3 achieves near-expert level on competition mathematics
- Competitive programming — R1 and o3 solve problems that stumped GPT-4
- Research synthesis — Connecting insights across papers, identifying gaps
- Debugging and code architecture — Planning complex refactors
- Logical puzzles and deduction — Anything requiring step-by-step reasoning
- Scientific reasoning — Hypothesis generation and experimental design
Stick With Standard Models For:
- Simple Q&A and classification — The answer is obvious, reasoning overhead wastes money
- High-volume, low-complexity tasks — Bulk text generation, summarization, translation
- Real-time applications — When latency matters more than quality
- Factual recall — Looking up specific information
- Formatting and rewriting — Structural changes, tone adjustments
Rule of thumb: If a human expert could solve it in under 30 seconds, a standard model is fine. If it requires pen-and-paper work or multiple steps, use a reasoning model.
Cost Optimization: o3-mini vs o3 vs R1
Reasoning models cost more due to extended thinking. Here's how to optimize:
The Cost Math
A single o3 query can generate 10,000+ thinking tokens. That's why actual costs vary wildly:
| Complexity | o3-mini | o3 | DeepSeek R1 |
|---|---|---|---|
| Simple (100 output tokens) | $0.005 | $0.02 | $0.003 |
| Medium (1K output tokens) | $0.05 | $0.15 | $0.03 |
| Hard (10K output tokens) | $0.50 | $1.50 | $0.30 |
| Research (50K tokens) | $2.50 | $7.50 | $1.50 |
Optimization Strategies
- Route by difficulty — Use o3-mini for medium problems, reserve o3 for hard ones
- Prompt compression — Shorter inputs = cheaper thinking
- Caching — Enable response caching for repeated query patterns
- Hybrid approaches — Use standard model for draft, reasoning model for verification
- Self-host R1 — If you have GPU infrastructure, running R1 locally eliminates API costs
When Each Model Wins on Cost
DeepSeek R1 is the clear winner for budget reasoning. At $0.55/$2.20, it's 2x cheaper than o3-mini and 12x cheaper than o3 for most tasks. The quality gap has narrowed significantly in 2026.
o3-mini offers the best OpenAI reasoning value at $1.10/$4.40 with 200K context. If you're already in the OpenAI ecosystem, it's the smart choice over full o3.
o3 (high) remains unmatched for research-grade reasoning but costs 12-50x more than alternatives. Reserve it for problems where the capability difference genuinely matters.
Multi-Agent Reasoning: Kimi K2.6 and Beyond
The latest frontier in reasoning models is multi-agent orchestration. Moonshot AI's Kimi K2.6 represents a new class of reasoning model designed for agentic workflows:
- Tool-use native — Baked-in function calling and API integration
- Stateful reasoning — Maintains context across agent exchanges
- Parallel planning — Explores multiple solution paths simultaneously
Other players in the multi-agent reasoning space include:
- Claude 4 Opus — Extended thinking for complex agentic tasks
- Gemini 3 Ultra — Google's 1M context enables massive agent memory
- GPT-5.2 Thinking — Strong for code agents requiring reasoning about large codebases
Frequently Asked Questions
What's the difference between o1, o3, and o3-mini?
o1 was OpenAI's first reasoning model (late 2024). o3 (2025) is a major leap in capability — achieving 87.5% on ARC-AGI vs o1's 25%. o3-mini (early 2025) offers o3-level reasoning at roughly 1/3 the cost, with 200K context vs o3's 128K. For most use cases, o3-mini is the sweet spot.
Is DeepSeek R1 as good as o3?
For most practical tasks, R1 is competitive with o1 and sometimes o3-mini. On math and coding benchmarks, R1 matches o1 performance. The main gaps are in very complex reasoning (where o3 still leads) and ecosystem integration (OpenAI's API is more mature). For pure value, R1 wins decisively.
Can I run reasoning models locally?
Yes, but with trade-offs. Qwen3 Thinking 32B runs on consumer GPUs (RTX 3090/4090) with ~30 tokens/second. Larger models like DeepSeek R1 70B require enterprise GPUs. Smaller distilled versions (7B-14B) offer decent reasoning on single GPUs but lag behind the full models.
How do I reduce reasoning model costs?
Four strategies: (1) Use difficulty routing — cheap model first, escalate only if needed. (2) Compress prompts — less input = less thinking. (3) Enable caching — reasoning patterns often repeat. (4) Self-host R1 if you have the infrastructure — API costs vanish but you pay for GPUs.
When should I NOT use reasoning models?
Avoid reasoning models for: simple Q&A, high-volume classification, real-time applications, bulk rewriting, and factual lookups. The extended thinking wastes compute and adds latency without benefit. Use standard models like GPT-4o mini or Gemini Flash for these tasks.
Key Takeaways
- Reasoning models (o3, R1) achieve genuine step-by-step problem solving — not just prompting tricks
- DeepSeek R1 offers the best value at $0.55/$2.20, competitive with o1 on most tasks
- o3-mini is the sweet spot for OpenAI reasoning at $1.10/$4.40 with 200K context
- Reserve o3 (high) for research-grade problems where capability genuinely matters
- Use the AI Token Cost Calculator to compare costs across reasoning models