Reasoning AI models: they think before answering, and bill for it

Reasoning
Services found: 169
#AI modelCapabilitiesAPI $/1M (in / out)Context windowPrice / moOutput $/1MOverviewVisit site
101

Kimi K2.6

Moonshot AI
Text & chatVisionReasoning
$0.95 / $4262.1K tokensAPI only4View
102

Claude Opus 4.6

Anthropic
Text & chatVisionReasoning
$5 / $251M tokensAPI only25View
103

GPT-5.6 Sol Pro

OpenAI
Text & chatVisionReasoning
$2 / $101.05M tokensAPI only10View
104

GPT-6 Astra Pro

OpenAI
Text & chatVisionReasoning
$10 / $501.05M tokensAPI only50View
105

Claude Opus 4.7

Anthropic
Text & chatVisionReasoning
$5 / $251M tokensAPI only25View
106

Qwen3.5-Flash

Qwen
Text & chatVisionVideo
$0.065 / $0.261M tokensAPI only0.26View
107

Kimi K2.5

Moonshot AI
Text & chatVisionReasoning
$0.45 / $2.25262.1K tokensAPI only2.25View
108

Qwen3.6 35B A3B

Qwen
Text & chatVisionVideo
$0.1 / $0.9262.1K tokensAPI only0.9View
109

GPT-5.2

OpenAI
Text & chatVisionReasoning
$1.75 / $14400K tokensAPI only14View
110

Qwen3.5-35B-A3B

Qwen
Text & chatVisionVideo
$0.3125 / $1.25262.1K tokensAPI only1.25View
111

GLM 5

Z.ai
Text & chatReasoningTool calling
$0.6 / $1.92204.8K tokensAPI only1.92View
112

Nemotron 3 Nano Omni

NVIDIA
Text & chatVisionAudio
$0 / $0256K tokensAPI only0View
113

Qwen3.5-9B

Qwen
Text & chatVisionVideo
$0.1 / $0.15262.1K tokensAPI only0.15View
114

Grok 4.20

xAI
Text & chatVisionReasoning
$1.25 / $2.52M tokensAPI only2.5View
115

Qwen3 VL 32B Instruct

Qwen
Text & chatVisionReasoning
$0.104 / $0.416131.1K tokensAPI only0.416View
116

MiniMax M2.5

MiniMax
Text & chatReasoningTool calling
$0.27 / $1.08204.8K tokensAPI only1.08View
117

Qwen3 VL 235B A22B Instruct

Qwen
Text & chatVisionReasoning
$0.21 / $1.9262.1K tokensAPI only1.9View
118

GPT-5.6 Terra Pro

OpenAI
Text & chatVisionReasoning
$2 / $121.05M tokensAPI only12View
119

GPT-5.1

OpenAI
Text & chatVisionReasoning
$1.25 / $10400K tokensAPI only10View
120

Qwen3.6 Plus

Qwen
Text & chatVisionVideo
$0.325 / $1.951M tokensAPI only1.95View

Browse by need

A reasoning model works through a problem before it answers, generating hidden thinking tokens along the way. Those tokens bill as output, which is why reasoning costs more than plain generation and why the price spread here - $0.25 to $50 per million - matters more than it looks.

169 models expose reasoning, including every current flagship: Claude Fable 5.1, GPT-6 Astra, Claude Opus 5. Use them for maths, multi-step analysis and code review. For lighter tasks a reasoning model is an expensive way to be slow. Filter for Adjustable thinking as well - 72 models let you cap how long the model deliberates.

Thinking bills as outputThe hidden deliberation is charged at the output rate, which is why the same prompt costs more here.
Depth is adjustableMost current reasoning models let you cap how long they think, turning quality into a dial.
Not always betterOn extraction and classification a reasoning model is slower and dearer for identical results.

Models with this capability

How to choose

  • Use reasoning where the first plausible answer is often wrong: maths, analysis, code review, planning.
  • Prefer models with an adjustable depth control so you can tune cost per task.
  • Watch output limits — deliberation plus answer can hit the max output ceiling on long tasks.
  • Compare on your own hard cases, not on benchmark scores.

Common mistakes

Leaving thinking on maximumDefault depth is rarely optimal. Most tasks finish just as well at a lower setting.
Using reasoning for formattingReshaping text into JSON needs structured output, not deliberation.

Who this suits

AnalystsMulti-step financial and legal reasoning where a missed condition is expensive.
Engineering teamsCode review, migration planning and debugging across many files.
Research workSynthesis across conflicting sources, where the model must weigh rather than summarise.

Frequently asked questions

Why do reasoning models cost more?

They generate hidden thinking tokens before the visible answer, and those tokens bill at the output rate. A hard question can produce several times more billed tokens than its answer suggests.

Can I limit how much a model thinks?

Yes on 72 of them. Claude, Grok, Qwen3 and DeepSeek V4 expose a depth control; DeepSeek switches thinking off entirely.

When is reasoning not worth it?

Classification, routing, extraction and summarising short text. A fast model does these as well for a tenth of the price.

Do reasoning tokens appear in the response?

No — you pay for them but do not see them. Some vendors expose a summary of the reasoning instead.

Is a reasoning model slower?

Noticeably. Expect seconds rather than milliseconds, which rules it out of autocomplete-style features.

Frequently searched

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