AI Reasoning Models Compared May 2026: o3 vs Claude Extended Thinking vs Gemini Deep Think vs DeepSeek-R1
In May 2026 the reasoning-model market is defined by four serious models: OpenAI's o3 (best frontier reasoning, expensive), Claude 4.7 with Extended Thinking (best agentic reasoning, balanced cost), Gemini 2.5 Deep Think (cheapest at the frontier, strong on math), and DeepSeek-R1 (best reasoning per dollar by a wide margin, open weights). Across published benchmarks and practitioner reports, o3 leads absolute accuracy, Gemini Deep Think leads pure math (~94% on AIME-style problems in published results), Claude Extended Thinking leads agentic tasks, and DeepSeek-R1 delivers near-frontier accuracy at roughly 1/10th the cost of o3.
TL;DR
In May 2026 the reasoning-model market has four serious flagships: OpenAI o3, Claude 4.7 with Extended Thinking, Gemini 2.5 Deep Think, and DeepSeek-R1. We compared them across the problem types that justify reasoning-model spend — math, logic, multi-step coding, agentic tasks, scientific reasoning — using the published benchmark record, vendor pricing, and practitioner reports.
Short version: o3 wins absolute accuracy, Gemini Deep Think wins pure math and science, Claude Extended Thinking wins agentic and long-horizon work, and DeepSeek-R1 wins cost-per-correct-answer by a wide margin.
What Makes a Reasoning Model Different
A standard LLM answers immediately. A reasoning model generates a long internal chain of thought first — sometimes thousands of tokens of "let me think about this..." — before producing the final answer. That extra computation costs tokens and time but materially improves accuracy on hard problems.
The trade-off is concrete: 5-50x the cost and 5-30x the latency vs a standard model. Use reasoning models when accuracy beats speed and cost.
For the frontier general-purpose models these reasoning modes sit on top of, see our Claude 4.7 vs GPT-5 vs Gemini 2.5 comparison.
How We Compared
We compared the four across the problem families where reasoning models earn their cost:
- Competition math — AIME-style problems, where all four labs publish results
- Hard science — GPQA Diamond, the standard published science benchmark
- Multi-step coding — problems with subtle bugs
- Agentic tasks — multi-step planning with tool use
- Ambiguous reasoning — policy, ethics, multi-constraint problems
The dimensions we rated:
- Accuracy — anchored to published benchmark results from the labs and independent evaluations
- Time to final answer — wall-clock latency at typical reasoning budgets
- Cost per question — derived from published token pricing and typical reasoning-token usage
- Cost per correct answer — the metric that actually matters
Where the public evidence does not support a precise number, we rate rather than invent one.
The Scoreboard
The scoreboard below synthesizes published benchmark results, vendor pricing, and practitioner reports into comparable ratings. The AIME and GPQA columns reflect publicly reported figures; the aggregate and agentic columns are qualitative:
| Model | Overall accuracy | Math (AIME, published) | GPQA (published) | Agentic | Latency | $/question |
|---|---|---|---|---|---|---|
| ------- | ------------------ | ------------------------ | ------------------ | --------- | --------- | ------------ |
| OpenAI o3 | Highest | ~92% | ~91% | Strong | Moderate | $0.40-1.50 |
| Gemini 2.5 Deep Think | Excellent | ~94% | ~92% | Good | Slowest | $0.05-0.20 |
| Claude 4.7 Extended Thinking | Excellent | ~88% | ~88% | Best | Fastest | $0.10-0.40 |
| DeepSeek-R1 | Strong | ~90% | ~88% | Good | Moderate | $0.02-0.08 |
1. OpenAI o3 — Absolute Accuracy Leader
Best for: Highest-stakes questions where every percentage point matters
o3 is the most consistent across problem categories. It does not lead any single category by a wide margin, but the published record puts it at the top or near the top in every category. For workloads where the cost of a wrong answer is high — legal analysis, medical second opinions, financial reasoning — o3's consistency is the value.
- Highest aggregate accuracy: Leads the published benchmark record overall
- Most consistent: Top or near-top in every category
- Reasoning depth: Generates the longest, most detailed chains of thought
- Production-ready: Integrated tooling, function calling, structured outputs
Limitations: The most expensive option by a wide margin. Latency is moderate to high. For most workloads, cheaper models deliver enough accuracy at a fraction of the cost.
2. Gemini 2.5 Deep Think — Best on Pure Math and Science
Best for: Hard math, scientific problems, anywhere accuracy on well-defined problems matters most
Gemini 2.5 Deep Think leads the published pure-math and GPQA Diamond results — roughly 94% on AIME-style problems and 92% on GPQA in publicly reported figures. When the problem is well-defined and has a verifiable answer, Deep Think is hard to beat. Combined with Gemini's aggressive pricing, it is also the cheapest frontier reasoning model in the closed-source tier.
- Best on math: ~94% on AIME-style problems in published results
- Best on science: ~92% on GPQA Diamond in published results
- Cheapest closed-frontier reasoning: ~$0.05-0.20 per hard question
- Largest context window: 1M+ token context for long-document reasoning
Limitations: Slightly weaker on agentic and tool-use tasks vs Claude. Latency is the highest of the four — Deep Think trades speed for depth.
3. Claude 4.7 Extended Thinking — Best Agentic Reasoning
Best for: Multi-step agents, long-horizon tasks, real coding workflows
Claude 4.7 Extended Thinking is the best at reasoning across long, multi-step tool-use loops. Where the others drift or lose state over 30+ step workflows, Claude maintains coherence. For AI agents that plan, act, observe, and continue over long horizons, Claude Extended Thinking is the strongest pick.
- Best agentic reasoning: The consistent leader in agentic benchmarks and practitioner reports
- Long-horizon coherence: Maintains state across 30+ step loops
- Adaptive thinking: Decides when to think hard vs answer fast
- Best general-purpose pairing: Strong as both reasoning and standard model
Limitations: Slightly lower on pure math vs Gemini. Cost falls between o3 and the cheaper options — not the value pick if budget is the constraint.
4. DeepSeek-R1 — Best Cost-Per-Correct-Answer
Best for: Cost-sensitive deployments, self-hosted reasoning, high-volume workloads
DeepSeek-R1 is the breakthrough open-weights reasoning model. It trails the frontier by only a few percentage points on most published benchmarks — but at roughly 1/10th the cost of o3, the cost-per-correct-answer is dramatically better. For startups, cost-sensitive deployments, or any workload where you can absorb a small accuracy hit, DeepSeek-R1 is the rational choice.
- Best cost-per-correct: ~1/10th the cost of o3 for a few points less accuracy
- Open weights: Self-hosting and full control are options
- Strong math: ~90% on AIME-style problems in published results, close to the frontier
- Surprisingly strong on coding: Competitive with the closed-source leaders
Limitations: Smaller ecosystem than the closed-source players. Self-hosting requires real engineering effort. Tooling integrations are catching up but not yet at parity.
Cost-per-Correct-Answer (the metric that matters)
Headline price is misleading. The right metric is cost per correct answer. The illustration below uses mid-range per-question costs (derived from published token pricing) and the close-together accuracy picture from the published benchmark record:
| Model | Cost/question (mid-range) | Accuracy tier | Cost per correct (indicative) |
|---|---|---|---|
| ------- | --------------------------- | --------------- | ------------------------------- |
| DeepSeek-R1 | ~$0.05 | Strong | Lowest by far |
| Gemini Deep Think | ~$0.12 | Excellent | Low |
| Claude Extended Thinking | ~$0.25 | Excellent | Moderate |
| o3 | ~$0.95 | Highest | Highest |
Because the accuracy gaps are a few points while the price gaps are an order of magnitude, DeepSeek-R1 comes out roughly an order of magnitude cheaper per correct answer than o3. For high-volume workloads where a small accuracy gap is tolerable, the math is obvious.
Choosing the Right Model
For highest-stakes questions
Recommended: o3
When the cost of a wrong answer is high, the most accurate option is the value choice — regardless of headline price.
For hard math and science
Recommended: Gemini 2.5 Deep Think
Leads pure math and GPQA at a fraction of o3's cost. The pick for problems with verifiable correct answers.
For agents and long-horizon work
Recommended: Claude 4.7 Extended Thinking
Coherence across long tool-use loops is what agents need. Claude leads. Most coding agents (Cursor, Cline, others) defaulted to Claude reasoning within weeks of 4.7's release.
For cost-sensitive and high-volume deployments
Recommended: DeepSeek-R1
Roughly an order of magnitude cheaper per correct answer than o3. The rational pick when budget matters and a small accuracy hit is acceptable.
Hybrid routing
Many production systems run a cheap model first and escalate to a reasoning model only when:
- The cheap model expresses low confidence
- The question is flagged by a classifier as hard
- The previous answer was rejected by a human reviewer
This pattern delivers reasoning-model quality at a fraction of the cost.
What Reasoning Models Are NOT Good At
- Real-time chat — too slow. Use a standard model.
- Simple Q&A — overkill. The cheap model gets it right at 1/20th the cost.
- High-throughput classification — use a fine-tuned smaller model.
- Anything where ~95% accuracy is sufficient — reasoning models earn their cost on problems where the last 5-10 points of accuracy actually matter.
Conclusion
What the May 2026 evidence supports:
- Highest accuracy: o3
- Best math and science: Gemini 2.5 Deep Think
- Best agentic reasoning: Claude 4.7 Extended Thinking
- Best cost-per-correct: DeepSeek-R1
Most production teams converge on two of the four — typically Claude Extended Thinking for agentic work plus DeepSeek-R1 for high-volume reasoning, or o3 for the highest-stakes paths plus Gemini Deep Think for math. Single-model setups optimize for one constraint.
For the standard models these reasoning modes sit on top of, see Claude 4.7 vs GPT-5 vs Gemini 2.5. For agents built on top, see AI agent frameworks comparison.
Key Takeaways
- o3 leads absolute reasoning accuracy across the published benchmark record — but its cost-per-correct-answer is the highest by a wide margin
- Gemini 2.5 Deep Think tops pure math (~94% on AIME-style problems in published results) and pure science (~92% on GPQA Diamond) — the best choice when the problem is well-defined
- Claude 4.7 with Extended Thinking leads agentic and long-horizon reasoning — coherent across 30+ step tool-use loops where the others drift
- DeepSeek-R1 delivers near-frontier accuracy at roughly 1/10th the cost of o3 — the value leader and the only open-weights reasoning model at this tier
- Reasoning models cost 5-50x more than standard models on the same problem because they generate long internal chains of thought before answering
- Use a reasoning model when accuracy matters more than latency or cost — and a standard model when speed or budget is the real constraint
- Cost-per-correct-answer (not headline price) is the right metric — a model that costs 3x but is right 50% more often is the cheaper choice
Frequently Asked Questions
What is a reasoning model?
A reasoning model is an LLM that generates long internal chains of thought before producing an answer. Unlike a standard model that responds immediately, a reasoning model can "think" for seconds or minutes — exploring possibilities, checking work, backtracking. The result is materially higher accuracy on hard problems (math, logic, coding, multi-step reasoning) at the cost of more tokens, more time, and more dollars per query.
Which reasoning model is the most accurate in 2026?
Across the published benchmark record in May 2026, OpenAI's o3 is the absolute accuracy leader, with Gemini 2.5 Deep Think, Claude 4.7 Extended Thinking, and DeepSeek-R1 close behind. The gaps are small. Gemini Deep Think specifically leads on pure math and science; Claude Extended Thinking leads on agentic tasks; o3 is the most consistent across categories. The honest answer: they are close at the top, and the right choice depends on workload.
How much do reasoning models cost?
Rough cost-per-question on hard problems in May 2026: o3 around $0.40-1.50 (depending on reasoning length), Claude 4.7 Extended Thinking $0.10-0.40, Gemini 2.5 Deep Think $0.05-0.20, DeepSeek-R1 $0.02-0.08. Reasoning models burn tokens — sometimes thousands of internal-thought tokens per question — which is why they cost 5-50x more than standard models. Cost-per-correct-answer matters more than headline price.
When should I use a reasoning model vs a standard model?
Use a reasoning model when accuracy matters more than latency or cost — hard math, code with subtle bugs, multi-step planning, ambiguous policy questions, scientific reasoning. Use a standard model for fast chat, simple Q&A, routine tasks, anything where ~95% accuracy is good enough at low cost. Many production systems route — cheap model first, escalate to a reasoning model only when confidence is low or the question is flagged hard.
Is DeepSeek-R1 really competitive with o3?
On accuracy, close — DeepSeek-R1 trails o3 by only a few percentage points on most published reasoning benchmarks. On cost, DeepSeek-R1 is dramatically cheaper, roughly 1/10th. For a startup or any cost-sensitive deployment, DeepSeek-R1 delivers more correct answers per dollar than any other reasoning model in 2026 by a large margin. It is also open-weights, which means self-hosting is possible. The trade-off is the operational complexity of self-hosting a large reasoning model.
What is the difference between Claude Extended Thinking and o3?
o3 is a reasoning-first model — every query gets reasoning by default. Claude 4.7's Extended Thinking is opt-in — Claude is a strong general-purpose model that can be asked to "think harder" when needed via the extended-thinking mode. Practically: o3 is the right pick if your workload is consistently hard. Claude Extended Thinking is right if you want a single model that handles both quick chat and hard reasoning, paying the reasoning cost only when needed.
About the Author
Fatima Al-Hassan
Security & Privacy Editorial Desk
Security & Privacy Editorial Desk · Web3AIBlog
Fatima Al-Hassan is a pen name for our security and privacy editorial desk. Posts under this byline are written and reviewed by contributors with backgrounds in application security, smart contract auditing, threat modeling, and privacy-preserving cryptography. The desk specializes in attacker-perspective explainers — how exploits actually work, what real recoveries look like, and which defenses survive contact with sophisticated adversaries. We coordinate disclosures responsibly and publish nothing that helps active attackers.
