Best Reasoning Model APIs in 2026: DeepSeek-R1, QwQ, o3, Claude Extended Thinking, Qwen3.7-Max Compared

Reasoning models think before they answer. Instead of pattern-matching a response in a single forward pass, they generate internal chain-of-thought steps — sometimes thousands of tokens of reasoning — before producing a final answer. This makes them dramatically better at math, code debugging, multi-step logic, and complex analysis compared to standard chat models.

In mid-2026, API developers have at least six serious reasoning model options across four providers. The field is moving fast: DeepSeek shipped V4 Pro and V4 Flash with 1M context, OpenAI still offers o3 and o4-mini alongside GPT-5, Anthropic added Extended Thinking to every Claude 4.x model, Alibaba launched Qwen3.7-Max with an "Agent Frontier" framing, and Microsoft entered with MAI-Thinking-1 in private preview.

We tested, priced, and compared them all. This page is the reference we wish existed when we were building TheRouter's reasoning model routing.

TL;DR — Reasoning Model Comparison Table

Prices retrieved June 2026. DeepSeek prices from api-docs.deepseek.com. OpenAI from openai.com/api/pricing. Anthropic from platform.claude.com. DashScope from help.aliyun.com. RMB → USD conversions approximate at ¥7.28/$.

What Makes a Model a "Reasoning Model"?

OpenAI-compatible means a provider exposes a chat-completions endpoint whose request and response shape matches the OpenAI API contract closely enough that an unmodified OpenAI SDK call works against it after swapping three values: API key, base URL, and model name. The minimum surface in practice is POST /v1/chat/completions with messages, model, and an OpenAI-shaped streaming response.

Reasoning models add an explicit chain-of-thought step. They allocate compute budget to thinking — generating intermediate reasoning tokens before the visible output. This is different from a standard chat model that goes straight from prompt to response.

The practical differences for API developers:

• Thinking tokens cost money. Most providers bill thinking tokens at output rates. A reasoning call that produces 200 visible output tokens might generate 2,000 thinking tokens behind the scenes — 10x the apparent output cost.
• Latency increases. First-token latency ranges from 3–30 seconds depending on problem complexity, vs. sub-second for standard models.
• Quality jumps on hard problems. On benchmarks like AIME 2025 (math competition), GPQA Diamond (graduate-level science), and SWE-bench (real-world code), reasoning models score 15–40% higher than their non-reasoning siblings.
• API shape may differ. Some models (DeepSeek, Qwen) let you toggle thinking on/off with enable_thinking: true. Others (OpenAI o-series) reason by default with no opt-out. Claude uses thinking: { type: "enabled", budget_tokens: N }.

Model-by-Model Breakdown

DeepSeek V4 Pro & V4 Flash

DeepSeek's V4 family is the price leader for reasoning APIs. V4 Pro ($0.435/$0.87 per million tokens) offers deep reasoning at roughly one-fifth the cost of OpenAI o3. V4 Flash ($0.14/$0.28) is even cheaper — competitive with standard chat models.

Both models support a 1 million token context window, the largest among reasoning models. They accept both OpenAI and Anthropic API formats, making integration straightforward with any client SDK.

Key facts from DeepSeek's pricing page:

• Thinking mode is on by default; toggle with enable_thinking: false
• Cache hit pricing is extremely aggressive: $0.003625/M (V4 Pro) and $0.0028/M (V4 Flash)
• Concurrency limits: 500 (V4 Pro) and 2,500 (V4 Flash)
• Both support function calling, JSON mode, and FIM completion (non-thinking only)

from openai import OpenAI

client = OpenAI(
    api_key="sk-...",
    base_url="https://api.deepseek.com"
)

response = client.chat.completions.create(
    model="deepseek-v4-pro",
    messages=[
        {"role": "user", "content": "Prove that √2 is irrational."}
    ],
    # Thinking mode is on by default
)
print(response.choices[0].message.content)

Best for: Teams that need strong reasoning at the lowest possible cost, and applications that benefit from 1M context (large codebases, long documents).

Note: The legacy model names deepseek-chat and deepseek-reasoner will be deprecated on July 24, 2026. Migrate to deepseek-v4-flash and deepseek-v4-pro.

OpenAI o3 and o4-mini

OpenAI's reasoning lineup includes o3 ($2.00/$8.00) as the flagship and o4-mini ($1.10/$4.40) as the cost-efficient option. Both use built-in chain-of-thought reasoning — there is no non-thinking mode.

o4-mini is particularly interesting: it outperforms o3-mini on most benchmarks while costing roughly half. For many reasoning workloads, o4-mini offers the best quality-per-dollar in OpenAI's lineup.

Key characteristics:

• 200K context window — smaller than DeepSeek's 1M but sufficient for most reasoning tasks
• Native function calling and structured outputs
• No thinking toggle — reasoning is always on
• Cached input pricing at 50% off ($1.00/$0.55 respectively)

from openai import OpenAI

client = OpenAI(api_key="sk-...")

response = client.chat.completions.create(
    model="o4-mini",
    messages=[
        {"role": "user", "content": "Find the bug in this function and explain your reasoning."}
    ]
)
print(response.choices[0].message.content)

Best for: Teams already invested in the OpenAI ecosystem, or workloads where OpenAI's safety and content filtering are requirements.

Claude Opus 4.6 with Extended Thinking

Anthropic approaches reasoning differently. Instead of separate reasoning models, they added Extended Thinking to their existing Claude models. Claude Opus 4.6 ($5.00/$25.00) with Extended Thinking enabled is Anthropic's strongest reasoning configuration.

What sets Extended Thinking apart from Anthropic's docs:

• Budget control. You set budget_tokens to cap how many thinking tokens the model can use — giving you cost control that o3 and DeepSeek don't offer.
• Thinking summaries. Claude 4.x models summarize their thinking process by default, so you can inspect reasoning without parsing raw chain-of-thought.
• 1M context available — but prompts over 200K tokens incur premium pricing ($10/$37.50 per million tokens).
• Same model, two modes: you toggle thinking on a per-request basis rather than calling a different model endpoint.

import anthropic

client = anthropic.Anthropic()

response = client.messages.create(
    model="claude-opus-4-6-20260205",
    max_tokens=16000,
    thinking={
        "type": "enabled",
        "budget_tokens": 10000
    },
    messages=[
        {"role": "user", "content": "Analyze this codebase for security vulnerabilities."}
    ]
)

Best for: Complex analysis, code review, and tasks where you need the highest quality and want explicit control over reasoning cost via budget_tokens.

Qwen3.7-Max (Thinking Mode)

Alibaba's Qwen3.7-Max launched May 19, 2026 as the "Agent Frontier" model. It supports both thinking and non-thinking modes, a 1M token context window, and both OpenAI and Anthropic API formats — making it one of the most flexible options.

Pricing on DashScope (China Mainland, from Alibaba Cloud pricing):

• Input: ¥12/million tokens (~$1.65 USD)
• Output: ¥36/million tokens (~$4.95 USD)
• Free tier: 1 million tokens within 90 days of account activation

The model scores competitively on reasoning benchmarks, with reported scores of 92.4 on GPQA Diamond and 97.1 on HMMT 2026 — positioning it near Claude Opus 4.6 territory on math and science tasks.

from openai import OpenAI

client = OpenAI(
    api_key="sk-...",
    base_url="https://dashscope.aliyuncs.com/compatible-mode/v1"
)

response = client.chat.completions.create(
    model="qwen3.7-max",
    messages=[
        {"role": "user", "content": "Solve this step by step."}
    ],
    extra_body={"enable_thinking": True}
)

⚠️ Qwen3.7-Max is not yet available in TheRouter's model catalog. We are tracking its addition. For now, call it directly via DashScope or route through a model fallback chain that includes DashScope as a provider.

Best for: Teams operating in the Alibaba Cloud ecosystem, long-context agent workflows, and developers who want OpenAI + Anthropic API compatibility from a single Chinese-hosted model.

QwQ-Plus (Sunsetting July 13, 2026)

QwQ-Plus is Alibaba's dedicated reasoning model at an extremely low price point — roughly ¥1/¥3 per million tokens (~$0.14/$0.41 USD). It has been a popular budget option for reasoning tasks.

However, QwQ-Plus is scheduled for deprecation on July 13, 2026. If you are currently using QwQ-Plus, we recommend migrating to Qwen3.7-Max with thinking mode or Qwen-Plus-Thinking as drop-in replacements. We covered the migration path in our July 13 sunset migration guide.

MAI-Thinking-1 (Microsoft — Private Preview)

Microsoft's first in-house reasoning model is a 35B-active, ~1T-total MoE that competes with Claude Opus 4.6 on SWE-Bench Pro and scores 97.0% on AIME 2025 and 94.5% on AIME 2026.

The catch: it is still in private preview on Azure Foundry as of June 2026. No public pricing has been announced. We covered MAI-Thinking-1 in more depth in our MAI-Thinking-1 vs DeepSeek comparison.

Best for: Enterprise teams already on Azure who need reasoning capabilities within the Microsoft compliance and data-residency stack. Wait for public preview and published pricing before committing production workloads.

Benchmark Comparison

Reasoning model benchmarks are notoriously difficult to compare across providers — each lab reports different subsets. Here is what we can verify from official sources:

Sources: Qwen3.7-Max benchmarks from Alibaba Cloud blog and third-party analysis. MAI-Thinking-1 from Microsoft AI. Claude from Anthropic. Not all providers report all benchmarks. Empty cells mean no verified data from official sources.

The key takeaway: on the benchmarks where multiple providers report, Qwen3.7-Max and Claude Opus 4.6 are within a few points of each other on reasoning-heavy tasks. DeepSeek V4 Pro and MAI-Thinking-1 are competitive but report fewer comparable data points.

Pricing Deep Dive — Cost per Reasoning Call

Raw per-token pricing only tells part of the story. Reasoning models generate substantial thinking tokens that inflate the effective cost. Here is a worked example for a medium-complexity reasoning task (8K input tokens, 500 visible output tokens, ~3,000 thinking tokens):

Thinking tokens billed at output rates for all models. DeepSeek and QwQ prices use cache-miss rates. Qwen3.7-Max converted at ¥7.28/$. Claude Opus 4.6 is the most expensive per call but offers budget_tokens control to cap thinking costs.

At 1,000 reasoning calls per day, the annual cost difference between DeepSeek V4 Flash ($730) and Claude Opus 4.6 ($46,720) is 64x. Choose your model based on the quality-cost tradeoff that fits your use case.

When comparing API pricing across providers, always normalize to USD per million tokens and split input from output. Most providers price output tokens 2–5× higher than input tokens, so a workload heavy on completion length looks very different from a retrieval-heavy workload at the same nominal "price per million."

• Use one currency (USD) — convert at publish date and cite the rate.
• Split input/output — never quote a single blended number.
• Cite each row to the provider's own pricing page with retrieval date.
• Note context-window tiers — long-context pricing often steps higher.

Decision Tree — Pick Your Reasoning Model

Use this decision tree to narrow your choice:

1. Need the lowest cost? → DeepSeek V4 Flash ($0.14/$0.28). The cheapest reasoning API by a wide margin.

2. Need the deepest reasoning + highest quality? → Claude Opus 4.6 with Extended Thinking. Set budget_tokens to control costs. Best output quality, highest price.

3. Need reasoning inside the OpenAI ecosystem? → o4-mini for cost efficiency, o3 for maximum capability.

4. Need 1M context for long documents or codebases? → DeepSeek V4 Pro/Flash or Qwen3.7-Max. Only these three offer 1M context with reasoning.

5. Operating in China / Alibaba Cloud? → Qwen3.7-Max via DashScope. Best pricing in RMB, free tier available, strong benchmarks.

6. Need enterprise Azure compliance? → Wait for MAI-Thinking-1 public preview, or use OpenAI o3/o4-mini via Azure OpenAI Service today.

7. Want to route across multiple reasoning models with automatic fallback? → Use TheRouter to configure a model fallback chain across providers.

Routing Across Reasoning Models with TheRouter

TheRouter routes OpenAI-compatible requests through configured providers and supports provider/model routing and fallback. For reasoning workloads, a common pattern is to set up a fallback chain that tries a cost-effective model first and escalates to a premium model if the first one fails or times out:

# Example: reasoning fallback chain
models:
  - id: deepseek/deepseek-v4-pro
    priority: 1
  - id: openai/o4-mini
    priority: 2
  - id: anthropic/claude-opus-4.6
    priority: 3

This routes reasoning requests to DeepSeek first (cheapest), falls back to o4-mini if DeepSeek is unavailable, and escalates to Claude Opus 4.6 for maximum quality. All three accept the same OpenAI-compatible request format.

Models available in TheRouter's catalog for reasoning workloads:

• DeepSeek V4 Pro
• DeepSeek V4 Flash
• DeepSeek R1
• OpenAI o3
• OpenAI o4-mini
• Claude Opus 4.6 (thinking)
• QwQ-Plus
• Qwen-Plus (thinking)

FAQ

What is a reasoning model?

A reasoning model generates internal chain-of-thought tokens before producing a visible answer. This extra compute step makes them significantly better at math, multi-step logic, coding, and complex analysis compared to standard chat models. The tradeoff is higher latency and cost.

Do thinking tokens count toward my bill?

Yes. All major providers bill thinking tokens at the output token rate. A reasoning call with 3,000 thinking tokens and 500 visible output tokens is billed for 3,500 output tokens. Claude's budget_tokens parameter lets you cap this cost.

Which reasoning model is cheapest?

DeepSeek V4 Flash at $0.14/$0.28 per million input/output tokens. At cache-hit rates ($0.0028/M input), it is one of the cheapest useful AI APIs of any kind.

Can I use reasoning models with the OpenAI SDK?

Yes. DeepSeek V4, Qwen3.7-Max, and MAI-Thinking-1 all support the OpenAI Chat Completions API format. You can use the standard openai Python or Node SDK by changing base_url and api_key. Claude Opus 4.6 supports both the Anthropic SDK and OpenAI-compatible endpoints via third-party routers.

Should I replace my standard model with a reasoning model?

Not always. Reasoning models are overkill for simple tasks (classification, extraction, summarization). Use them for problems that benefit from multi-step thinking: complex math, code debugging, legal analysis, scientific reasoning. For everything else, standard models like GPT-4.1 or DeepSeek V4 Flash in non-thinking mode are faster and cheaper.

What happens when QwQ-Plus sunsets on July 13?

Alibaba will discontinue the QwQ-Plus model on July 13, 2026. Recommended replacements: Qwen3.7-Max (thinking mode) for maximum capability, or Qwen-Plus-Thinking for a budget option. See our migration guide for step-by-step instructions.

Can I route reasoning requests across multiple providers?

Yes. TheRouter supports model fallback routing across providers. Configure a priority chain (e.g., DeepSeek → o4-mini → Claude) and TheRouter automatically routes to the next provider if the primary is unavailable or returns an error.