Gemini Robotics-ER 1.6 vs Gemini 3.1 Flash-Lite
Benchmarks, API Token Pricing, Context Window & Speed Comparison
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Gemini Robotics-ER 1.6 by Google and Gemini 3.1 Flash-Lite by Google represent two of the frontier AI models analyzed in our comprehensive 2026 AI benchmark and LLM comparison. In this head-to-head evaluation, we examine API token pricing, context window specifications, inference speed (tokens per second), and empirical reasoning performance. From a pure economic perspective, Gemini 3.1 Flash-Lite holds a notable pricing advantage for token ingestion. On output generation, Gemini 3.1 Flash-Lite is more economical per million generated tokens. In data-heavy architectures requiring extensive repository or document analysis, Gemini 3.1 Flash-Lite provides an expanded memory horizon with 1,049K tokens. In latency-sensitive production environments, Gemini 3.1 Flash-Lite leads in tokens-per-second throughput, making it ideal for real-time conversational agents and high-concurrency workflows. Below is the complete empirical head-to-head breakdown across cost, speed, context, and capability matrices.
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Gemini Robotics-ER 1.6
Raisonnement incarnéEmbodied raisonnement VLM développé pour 3D spatial planning, robotic manipulation, et physical tâche coordination.
$2.50 / 1M
$10.00 / 1M
131.1K tokens
N/A
Gemini 3.1 Flash-Lite
Lite à haut volumecoût-optimized élevé-volume Flash modèle développé pour responsive interactif chatbots et extraction pipelines.
$0.15 / 1M
$0.60 / 1M
1.05M tokens
N/A
Différences Clés en un Coup d’Œil
- Gemini 3.1 Flash-Lite is 16.5x cheaper for API usage (input + output tokens combined).
- Gemini 3.1 Flash-Lite has a larger context window (1M vs 131K tokens).
- Gemini 3.1 Flash-Lite is faster at 250 tok/s vs 90 tok/s.
| Caractéristique | Gemini Robotics-ER 1.6 | Gemini 3.1 Flash-Lite |
|---|---|---|
Intelligence Index Composite reasoning & knowledge score (0-100) | 82 | 60 |
Provider | ||
Input Price Cost per million input tokens | $2.50 / 1M | $0.15 / 1M |
Output Price Cost per million output tokens | $10.00 / 1M | $0.60 / 1M |
Blended Price Weighted 7:2:1 cache:input:output per 1M | $1.85 / 1M | $0.11 / 1M |
Context Window Maximum input + output tokens | 131.1K tokens~197 A4 pages of text | 1.05M tokens~1,573 A4 pages of text |
Max Output Tokens Per single request | 16K | 33K |
Throughput Speed Tokens generated per second | 90 tok/s | 250 tok/s |
SWE-bench Verified Real-world software engineering | N/A | N/A |
Cached Input Price Prompt caching discount per 1M tokens | $0.50 / 1M | $0.04 / 1M |
Qui gagne où ?
At-a-glance decision frameworkEvaluations ongoing
Scores de Benchmarks Vérifiés
Official evaluation metrics verified by independent third-party research labs.
Hardened reasoning and academic knowledge benchmark with reduced lucky-guess probability.
Crowdsourced human preference evaluation based on blind, head-to-head model battles.
Tokens emitted per second during active generation streaming.
Latence et Temps de Réponse
Time to first token (TTFT) and estimated end-to-end response time for a typical 500-token response.
Simulateur Interactif de Coût de Tokens
Adjust sliders to calculate projected monthly cloud API spending with prompt caching discounts.
Gemini Robotics-ER 1.6 · Strengths & Key Advantages
- Groundbreaking 3D spatial coordinate raisonnement
- Planification directe de trajectoire matérielle
- Récupération robuste face aux défaillances physiques
Considerations & Trade-offs
- spécialisé robotics focus — pas intended pour général chat
- supérieur coût par visuel frame
Gemini 3.1 Flash-Lite · Strengths & Key Advantages
- Extremely faible coût par request
- Blazing rapide 250 tps diffusion continue
- 1M fenêtre de contexte
Considerations & Trade-offs
- inférieur complexe raisonnement capacité que Pro
- Simplified appel d'outils constraints
Token Use & Task Economics
Estimated cost and token consumption to complete one representative agentic task.
| Metric | Gemini Robotics-ER 1.6 | Gemini 3.1 Flash-Lite |
|---|---|---|
Cost per Task Estimated USD to finish one task | $1.80 | $0.38 |
Output Tokens / Task Tokens generated per task | 12K | 14K |
Reasoning Tokens / Task Thinking tokens burned per task | 6K | 5K |
Spécifications Techniques
Architecture, release timeline, capabilities, and modalities.
| Caractéristique | Gemini Robotics-ER 1.6 | Gemini 3.1 Flash-Lite |
|---|---|---|
| Official API Model ID (SDK) | models/gemini-robotics-er-1-6 | models/gemini-3-1-flash-lite |
| API Protocol | Google Gemini API (generateContent) + OpenAI-Compatible SDK | Google Gemini API (generateContent) + OpenAI-Compatible SDK |
| Release Date | 2026-04 | 2026-03 |
| Knowledge Cutoff | February 2026 | November 2025 |
| Architecture | Embodied Spatial Vision-Language-Action (VLA) Model | Compact efficient multimodal transformer |
| Parameters | Confidential | Confidential |
| Enterprise Rate Limits (RPM / TPM) | 10,000 – 30,000 RPM (Tier 5 Enterprise) · 800,000 – 2,000,000 TPM | 10,000 – 30,000 RPM (Tier 5 Enterprise) · 4,000,000 – 10,000,000 TPM |
| Long-Context Tier (>128K/200K) | Flat rate ($2.5/1M in) across full 131K window | Standard rate up to 200K tokens; 2x input/output tier above 200K tokens |
| Prompt Cache Policy & TTL | Implicit + Explicit Context Caching at $0.5/1M with configurable TTL | Implicit + Explicit Context Caching at $0.035/1M with configurable TTL |
| Computer Use / GUI Agent | Supported (Screen coordinate grounding, browser & desktop GUI action loops) | Supported (Screen coordinate grounding, browser & desktop GUI action loops) |
| MCP & Parallel Tool Calling | Native MCP Client/Server ready · Parallel function calling · 100% Strict JSON Schema | Native MCP Client/Server ready · Parallel function calling · 100% Strict JSON Schema |
| Web Search Grounding & Sandbox | Built-in Google Search Grounding + Managed Python Code Execution Sandbox | Built-in Google Search Grounding + Managed Python Code Execution Sandbox |
| Official Fine-Tuning Support | Vertex AI Supervised Adapter Tuning & Context Distillation | Vertex AI Supervised Adapter Tuning & Context Distillation |
| Multimodal Token Billing | Text BPE (~4 chars/token) · Vision (~258–765 tokens per image tile) | Text BPE (~4 chars/token) · Vision (~258–765 tokens per image tile) |
| Quantization & Min GPU VRAM | Cloud-Hosted Proprietary (FP8 / BF16 Tensor Core Cluster) · Zero Local VRAM Required (100% Serverless Cloud API) | Cloud-Hosted Proprietary (FP8 / BF16 Tensor Core Cluster) · Zero Local VRAM Required (100% Serverless Cloud API) |
| Safety & Frontier Classification | Google DeepMind Frontier Safety Framework Verified | Google DeepMind Frontier Safety Framework Verified |
| Enterprise Compliance (ZDR/HIPAA/SOC2) | SOC2 Type II · GDPR · Zero Data Retention (ZDR) & HIPAA BAA Available on Enterprise Tier | SOC2 Type II · GDPR · Zero Data Retention (ZDR) & HIPAA BAA Available on Enterprise Tier |
| Input Modalities | text, vision | text, vision |
| Output Modalities | text | text |
Gemini Robotics-ER 1.6 vs Gemini 3.1 Flash-Lite: Réponses Rapides (FAQ)
Which is more intelligent, Gemini Robotics-ER 1.6 or Gemini 3.1 Flash-Lite?
Gemini Robotics-ER 1.6 is more intelligent. It scores 82 compared with 60 on the composite Intelligence Index.
Which model is faster?
Gemini 3.1 Flash-Lite is faster, generating 250 tokens per second compared with 90 tokens per second.
Which model is cheaper to run?
Gemini 3.1 Flash-Lite is cheaper at $0.11 per 1M tokens blended, compared with $1.85 (7:2:1 cache:input:output ratio).
Which model has lower latency?
Gemini 3.1 Flash-Lite has lower latency, with a time to first token of 0.27s compared with 0.70s.
Which model has a larger context window?
Gemini 3.1 Flash-Lite has a larger context window, supporting 1.05M tokens compared with 131.1K tokens.
Verdict
Verdict & Model Selection Recommendation: Choose Gemini Robotics-ER 1.6 if your priority is Autonomous warehouse robots, humanoid task planning, and physical laboratory automation. Its performance in demanding tasks justifies its operational token cost. Choose Gemini 3.1 Flash-Lite if your workflow prioritizes High-throughput microservices, translation, and large-scale document parsing. For large-scale batch processing or customer-facing conversational interfaces, its balance of speed and economics delivers superior ROI.
Questions fréquentes
How does Gemini Robotics-ER 1.6 compare to Gemini 3.1 Flash-Lite in API token pricing and cost per token?+
Gemini Robotics-ER 1.6 costs $2.50 per 1M input tokens and $10.00 per 1M output tokens, whereas Gemini 3.1 Flash-Lite costs $0.15 input and $0.60 output. Overall, Gemini 3.1 Flash-Lite is the more cost-effective model for high-volume API pipelines. Both models support prompt caching discounts of up to 90% for repeated prompt prefixes.
Which model has a larger context window: Gemini Robotics-ER 1.6 or Gemini 3.1 Flash-Lite?+
Gemini Robotics-ER 1.6 supports a context window of 131K tokens (~98k words), while Gemini 3.1 Flash-Lite supports 1,049K tokens (~786k words). Therefore, Gemini 3.1 Flash-Lite can process more textual data and document context in a single prompt.
Which AI model is better for software engineering and coding: Gemini Robotics-ER 1.6 or Gemini 3.1 Flash-Lite?+
Gemini Robotics-ER 1.6 scores at the frontier of coding benchmarks, while Gemini 3.1 Flash-Lite scores competitively for software generation. For complex autonomous debugging and agentic engineering, Gemini Robotics-ER 1.6 provides robust code generation and tool calling.
What is the difference in reasoning capability and extended thinking between Gemini Robotics-ER 1.6 and Gemini 3.1 Flash-Lite?+
Gemini Robotics-ER 1.6 features native chain-of-thought extended thinking mode with configurable reasoning effort. In contrast, Gemini 3.1 Flash-Lite provides chain-of-thought reasoning depth for complex math and science.
How does inference speed (tokens per second) compare between Gemini Robotics-ER 1.6 and Gemini 3.1 Flash-Lite?+
Gemini Robotics-ER 1.6 generates approximately 90 tokens per second, while Gemini 3.1 Flash-Lite delivers 250 tokens per second. In latency-sensitive production environments, Gemini 3.1 Flash-Lite provides faster time-to-first-token (TTFT) and higher streaming response velocity.
Can you implement a tiered LLM routing setup combining Gemini Robotics-ER 1.6 and Gemini 3.1 Flash-Lite to optimize costs?+
Yes! High-efficiency architectures route high-frequency routine classification and conversational tasks to Gemini 3.1 Flash-Lite to save operational costs, and dynamically escalate complex architectural queries or reasoning tasks to Gemini Robotics-ER 1.6. This tiered routing pattern cuts overall API bills by up to 70%.
Understand key metrics like context window, token pricing, reasoning mode, and SWE-bench scores in our comprehensive AI glossary.
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Les scores de benchmarks, tarifs par token et limites évoluent rapidement. Vérifiez toujours les spécifications actuelles sur la documentation officielle du fournisseur.
Sources: GetInfoUs Independent AI Benchmarks & Official API Specs