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Put up to 4 models beside each other — token prices, context windows, capabilities and provider, from the same catalogue the model pages read.

  1. GPT Image 2.6 FlareOpenAIEnterprise & partner accessRemove
  2. Claude Fable 5.2AnthropicRemove
  3. Nemotron 3.5 Super VL (Free)NVIDIARemove
gpt-image-2.6-flare vs claude-fable-5.2 vs nemotron-3.5-super-vl:free
AttributeGPT Image 2.6 Flaregpt-image-2.6-flareClaude Fable 5.2claude-fable-5.2Nemotron 3.5 Super VL (Free)nemotron-3.5-super-vl:free
Pricing
Input$5.00 / 1M$8.00 / 1M$0 / 1M
Output$30.00 / 1M$40.00 / 1M$0 / 1M
Cache Write (5m)Not applicable$10.00 / 1M—
Cache Write (1h)Not applicable$16.00 / 1M—
Cache ReadNot applicable$0.20 / 1M$0 / 1M
Web Search—$0 / 1M—
Cache Write——$0 / 1M
Context
Max contextN/A1M1M
Max outputN/AN/AN/A
Capabilities
VisionYesYesYes
Function CallingNoYesYes
JSON ModeNoYesYes
StreamingNoYesYes
Catalogue
ProviderOpenAIAnthropicNVIDIA
Categoryimagechatchat
Charge typePay As You GoPay As You GoFree
Released———
Description
SummaryOpenAI GPT Image 2.6 Flare. Billed by tokens at OpenAI's price: text input US$5, image input US$8 and image output US$30 per 1M tokens.Claude Fable 5.2 is Anthropic's most capable model for advanced technical work and open-ended problem solving, delivering significant improvements in coding, reasoning, and vision over Fable 5.1. It excels at exceptionally complex tasks, including large-scale software migrations, electronic system design, and advanced scientific and mathematical research. With up to 30% faster output generation, clearer communication, and always-on adaptive extended thinking, Fable 5.2 supports configurable reasoning effort from low to max (default high), with thinking preserved across turns. Designed for frontier engineering, scientific discovery, and ambitious long-horizon agentic workflows, it complements Opus 5.5 as a specialized option for the most demanding technical challenges.NVIDIA Nemotron 3.5 Super VL is an open multimodal Mixture-of-Experts (MoE) model featuring 120B total parameters with 12B activated per token. Built on a hybrid Mamba–Transformer architecture with multi-token prediction, it supports text, images, documents, GUI screenshots, and video inputs, along with a 1M-token context window for complex, long-horizon workflows. Optimized for multimodal reasoning, visual understanding, and agentic tool use, the model supports configurable reasoning modes and token budgets. It is well suited for document intelligence, web research, coding validation, customer service, GUI analysis, and video understanding, enabling efficient, long-running multimodal agent applications.