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Gemini 3.1 Flash vs UI-TARS 7B

How do these models stack up? Below is an expert side-by-side comparison of specifications, context window capacity, live pricing per million tokens, and standardized benchmark scores for Gemini 3.1 Flash and UI-TARS 7B.

Google

Gemini 3.1 Flash

Gemini 3.1 Flash is Google's high-speed, cost-efficient multimodal model in the 3.1 generation, purpose-built for high-volume content synthesis, classification, and intelligent routing at scale. Featuring a 1-million-token context window, it can process large batches of documents, customer data, or multimedia content in a single inference pass, dramatically reducing pipeline complexity. At just $0.25/MTok for input, it is one of the most affordable routes to Google-caliber multimodal AI, making it an ideal backbone for production pipelines, data enrichment workflows, and high-frequency API integrations.

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ByteDance

UI-TARS 7B

UI-TARS-1.5 is a multimodal vision-language agent optimized for GUI-based environments, including desktop interfaces, web browsers, mobile systems, and games. Built by ByteDance, it builds upon the UI-TARS framework with reinforcement...

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Technical Specifications

SpecificationGemini 3.1 FlashUI-TARS 7B
ProviderGoogleByteDance
Context Window1,000,000 tokens128,000 tokens
Agent Suitability86/100N/A
Time to First Token (TTFT)150 msN/A
Deployment Modelmanaged apimanaged api
Production Stabilitystablestable
API AvailableYesYes
Released Date2026-04-202025-07-22

API Pricing Comparison

Input Price per Million Tokens

Gemini 3.1 Flash

$0.25

UI-TARS 7B

$0.10

Output Price per Million Tokens

Gemini 3.1 Flash

$1.50

UI-TARS 7B

$0.20

Want to test both models live?

Run side-by-side prompt prompts in our dynamic Sandbox. Check execution speeds, latency metrics, and compute actual costs in real-time.

Benchmark Performance Metrics

Scores show the raw performance percentages verified across key evaluation suites. Higher bars indicate superior accuracy and capability in that domain.

MMLUGeneral knowledge & multi-task understanding
8680.0%vs7350.0%
Gemini 3.1 Flash
UI-TARS 7B
HumanEvalPython coding & logic synthesis
8850.0%vs8020.0%
Gemini 3.1 Flash
UI-TARS 7B
MATHComplex mathematical problem solving
7820.0%vs4050.0%
Gemini 3.1 Flash
UI-TARS 7B
GPQAGraduate-level expert reasoning
6050.0%vs3000.0%
Gemini 3.1 Flash
UI-TARS 7B
HellaSwagCommonsense reasoning and inference
9520.0%vs7800.0%
Gemini 3.1 Flash
UI-TARS 7B
MT-BenchMulti-turn conversation flow quality
900.0%vs805.0%
Gemini 3.1 Flash
UI-TARS 7B

Gemini 3.1 Flash Quirks & Gotchas

  • โ–ธMost cost-effective Google model โ€” ideal for high-volume pipelines
  • โ–ธContext caching available via Vertex AI for repeated document processing

UI-TARS 7B Quirks & Gotchas

No developer gotchas reported.