Adjustable thinking AI models: set the reasoning budget per request

Adjustable thinking
Services found: 79
#AI modelCapabilitiesAPI $/1M (in / out)Context windowPrice / moOutput $/1MOverviewVisit site
61

Qwen3.6 Flash

Qwen
Text & chatVisionVideo
$0.1875 / $1.1251M tokensAPI only1.125View
62

Qwen3.5-27B

Qwen
Text & chatVisionVideo
$0.195 / $1.56262.1K tokensAPI only1.56View
63

Qwen3 Next 80B A3B Instruct

Qwen
Text & chatReasoningAdjustable thinking
$0.09 / $1.1262.1K tokensAPI only1.1View
64

Qwen3 VL 30B A3B Instruct

Qwen
Text & chatVisionReasoning
$0.15 / $0.6262.1K tokensAPI only0.6View
65

Nano Banana 2 (Gemini 3.1 Flash Image)

Google
VisionAdjustable thinkingImage generation
$0.5 / $3131.1K tokensAPI only3View
66

Qwen3 Max

Qwen
Text & chatReasoningAdjustable thinking
$0.78 / $3.9262.1K tokensAPI only3.9View
67

Qwen3 Coder Flash

Qwen
Text & chatVisionCoding
$0.195 / $0.9751M tokensAPI only0.975View
68

Nano Banana 2 Lite (Gemini 3.1 Flash Lite Image)

Google
VisionAdjustable thinkingImage generation
$0.25 / $1.565.5K tokensAPI only1.5View
69

Qwen3.5 Plus 2026-02-15

Qwen
Text & chatVisionVideo
$0.26 / $1.561M tokensAPI only1.56View
70

Qwen3.5 Plus 2026-04-20

Qwen
Text & chatVisionVideo
$0.3 / $1.81M tokensAPI only1.8View
71

Qwen3 Coder Plus

Qwen
Text & chatCodingReasoning
$0.65 / $3.251M tokensAPI only3.25View
72

Nano Banana Pro (Gemini 3 Pro Image)

Google
VisionAdjustable thinkingImage generation
$2 / $12131.1K tokensAPI only12View
73

Qwen3 Max Thinking

Qwen
Text & chatReasoningAdjustable thinking
$0.78 / $3.9262.1K tokensAPI only3.9View
74

Qwen3.8 Omni Flash

Qwen
Text & chatVisionAudio
1M tokensAPI onlyView
75

Hy4 preview

Tencent
Text & chatReasoningAdjustable thinking
$0.84 / $2.521M tokensAPI only2.52View
76

Fugu Max

Sakana
Text & chatReasoningAdjustable thinking
$2 / $6API only6View
77

Solar Mini 4

Upstage
Text & chatReasoningAdjustable thinking
$0.1 / $0.4512K tokensAPI only0.4View
78

Perceptron Mk1.5

Perceptron
Text & chatVisionAudio
$0.15 / $1.536.9K tokensAPI only1.5View
79

Ternary Bonsai 2 27B

PrismML
Text & chatVisionReasoning
262.1K tokensAPI onlyView

Browse by need

Eighty-five models let you set how long the model deliberates before answering. Because thinking tokens bill as output, that dial is a direct cost control: the same model can be cheap and quick or slow and thorough depending on a single parameter.

The Claude family exposes it most clearly, and Grok, Qwen3 and DeepSeek V4 offer their own variants - DeepSeek switches between thinking and non-thinking modes outright. Set the dial per task rather than per application: minimal for classification and extraction, generous for architecture decisions and debugging.

Quality as a dialThe same model can be cheap and quick or slow and thorough on demand.
Per task, not per appExtraction wants minimal depth; architecture decisions want maximum.
Direct cost controlBecause thinking bills as output, the dial moves the invoice immediately.

Models with this capability

How to choose

  • Profile your task mix before fixing a default depth.
  • Use minimal settings for anything deterministic — parsing, routing, classification.
  • Reserve deep settings for ambiguous problems where a wrong answer is expensive.
  • Log which depth produced which outcome so the setting can be tuned with evidence.

Common mistakes

One global settingA single depth for every request overpays on easy work and underdelivers on hard work.
Turning thinking off entirelyOn genuinely hard tasks it costs more in retries than it saves in tokens.

Who this suits

Cost-sensitive productsFixed budgets with variable task difficulty.
Agent buildersCapping deliberation keeps multi-step runs from spiralling.
Mixed workloadsOne model serving both trivial and demanding requests.

Frequently asked questions

How does the dial save money?

Thinking tokens bill as output. A shallow setting on the same model can cost a fraction of a deep one.

Which models expose it?

The Claude family most clearly, plus Grok, Qwen3 and DeepSeek V4, which switches thinking off entirely.

Should it be set per app or per task?

Per task. Minimal for extraction and classification, generous for architecture decisions and debugging.

Which vendors expose the control?

Anthropic, xAI, Alibaba and DeepSeek, each with its own naming.

Is low depth the same as a smaller model?

No. A large model thinking briefly still brings its full knowledge to the answer.

Frequently searched

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