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Compare models

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

  1. Devstral 2 2512Mistral AIRemove
  2. GPT-6.1 TerraOpenAIRemove
  3. Nemotron 3.5 Super VLNVIDIARemove
devstral-2512 vs gpt-6.1-terra vs nemotron-3.5-super-vl
AttributeDevstral 2 2512devstral-2512GPT-6.1 Terragpt-6.1-terraNemotron 3.5 Super VLnemotron-3.5-super-vl
Pricing
Input$0.15 / 1M$1.00 / 1M$0.10 / 1M
Output$0.60 / 1M$5.00 / 1M$0.40 / 1M
Cache Write (5m)$0.15 / 1M$1.00 / 1M$0.10 / 1M
Cache Write (1h)$0.15 / 1M$1.00 / 1M$0.10 / 1M
Cache Read$0.15 / 1M$1.00 / 1M$0.10 / 1M
Web Search$0 / 1M$0 / 1M$0 / 1M
Context
Max context262.1K1M1M
Max outputN/AN/AN/A
Capabilities
VisionNoYesYes
Function CallingYesYesYes
JSON ModeYesYesYes
StreamingYesNoYes
Catalogue
ProviderMistral AIOpenAINVIDIA
Categorychatchatchat
Charge typePay As You GoPay As You GoPay As You Go
Released———
Description
SummaryDevstral 2 is Mistral AI's open-source, agentic coding model — a 123B dense transformer with a 256K context window. It can navigate and modify large codebases, coordinate multi-file changes, manage dependencies, and automatically detect and fix errors. Designed for modernization and large engineering tasks, it’s tunable for specific languages or enterprise code and is released under a modified MIT license.GPT-6.1 Terra is OpenAI’s mid-tier model in the GPT-6.1 series, designed to balance capability, speed, and cost efficiency. Positioned below GPT-6.1 Sol, it delivers strong performance across everyday agentic workflows, coding, and professional writing, with particular strengths in screenshot understanding and on-screen content analysis. Optimized for high-volume production workloads, GPT-6.1 Terra is well suited for coding assistants, visual interface analysis, workflow automation, and scalable agentic applications that require reliable performance at an accessible cost.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.