Head-to-Head Model Comparison

Gemini 3.5 Flash-Lite vs Claude Opus 5.5

Benchmarks, API Token Pricing, Context Window & Speed Comparison

Revisado por el equipo de investigación de GetInfoUsÚltima revisión: October 2026Política editorial
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GoogleAnthropiclightweightflagship

Compare Gemini 3.5 Flash-Lite by Google against Claude Opus 5.5 by Anthropic across real-world coding, reasoning, token economics, and latency.

Comparación Directa (VS)

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Google

Gemini 3.5 Flash-Lite

Flash eficiente

sub-dollar multimodal razonamiento motor tuned para masivo empresarial traffic y en tiempo real subagents.

Intelligence Index69 / 100
Input Token:

$0.40 / 1M

Output Token:

$2.00 / 1M

Context Window:

1.05M tokens

SWE-bench Verified:

N/A

Anthropic

Claude Opus 5.5

Titán de inteligencia de frontera

de Anthropic pinnacle modelo de frontera diseñado para de largo alcance agentic ingeniería de software, denso multimodal documento síntesis, y autónomo investigación flujos de trabajo.

Intelligence Index94 / 100
Input Token:

$4.00 / 1M

Output Token:

$20.00 / 1M

Context Window:

1M tokens

SWE-bench Verified:

84.6%

Diferencias Clave de un Vistazo

  • Gemini 3.5 Flash-Lite is 9.6x cheaper for API usage (input + output tokens combined).
  • Gemini 3.5 Flash-Lite has a larger context window (1M vs 1M tokens).
  • Gemini 3.5 Flash-Lite is faster at 240 tok/s vs 78 tok/s.
Need definitions for token pricing, context, or benchmarks?Explore AI Glossary →
CaracterísticaGemini 3.5 Flash-LiteClaude Opus 5.5
Intelligence Index
Composite reasoning & knowledge score (0-100)
6994
Provider
GoogleAnthropic
Input Price
Cost per million input tokens
$0.40 / 1M$4.00 / 1M
Output Price
Cost per million output tokens
$2.00 / 1M$20.00 / 1M
Blended Price
Weighted 7:2:1 cache:input:output per 1M
$0.35 / 1M$3.08 / 1M
Context Window
Maximum input + output tokens
1.05M tokens~1,573 A4 pages of text1M tokens~1,500 A4 pages of text
Max Output Tokens
Per single request
33K128K
Throughput Speed
Tokens generated per second
240 tok/s78 tok/s
SWE-bench Verified
Real-world software engineering
N/A84.6%
GPQA Diamond
Graduate-level reasoning
N/A82.3%
Cached Input Price
Prompt caching discount per 1M tokens
$0.10 / 1M$0.40 / 1M

¿Quién gana en cada área?

At-a-glance decision framework
Código

Evaluations ongoing

Costo
Gemini 3.5 Flash-Lite
9.6x cheaper monthly runtime
Velocidad
Gemini 3.5 Flash-Lite
+208% faster generation
Contexto
Gemini 3.5 Flash-Lite
1.05M tokens window

Puntuaciones de Benchmarks Verificadas

Official evaluation metrics verified by independent third-party research labs.

Gemini 3.5 Flash-LiteClaude Opus 5.5
SWE-bench Verified (Autonomous Coding)

Industry benchmark evaluating real-world GitHub issue resolution and multi-file debugging.

N/Avs84.6%
GPQA Diamond (Doctorate-Level Science & Math)

Challenging questions created by domain experts to test complex PhD-level reasoning.

N/Avs82.3%
MMLU-Pro (Multi-task Language Understanding)

Hardened reasoning and academic knowledge benchmark with reduced lucky-guess probability.

81.2%vs91.5%
LMSYS Chatbot Arena Elo

Crowdsourced human preference evaluation based on blind, head-to-head model battles.

1375ptsvs1507pts
Humanity's Last Exam (Frontier Expert Knowledge)

Extremely difficult, closed-ended questions spanning the frontier of human expert knowledge.

N/Avs19.8%
Terminal-Bench (Agentic Coding & Terminal Use)

Autonomous multi-step tasks executed inside a real command-line terminal environment.

N/Avs66.4%
SciCode (Scientific Computing)

Research-grade coding problems drawn from real scientific and numerical workloads.

N/Avs58.7%
LiveCodeBench (Competitive Coding)

Contamination-free competitive programming problems collected continuously over time.

N/Avs83.1%
AIME (Advanced Math Competition)

American Invitational Mathematics Examination — multi-step Olympiad-level reasoning.

N/Avs95.4%
Long-Context Reasoning

Accuracy when reasoning over very large inputs that fill the model context window.

N/Avs92.6%
Generation Speed (Throughput)

Tokens emitted per second during active generation streaming.

240 tok/s vs78 tok/s

Latencia y Tiempo de Respuesta

Time to first token (TTFT) and estimated end-to-end response time for a typical 500-token response.

Time to First Token (TTFT)Lower is better
Gemini 3.5 Flash-Lite0.29s
Claude Opus 5.51.40s
Est. End-to-End Response (500 tokens)TTFT + generation time
Gemini 3.5 Flash-Lite2.37s
Claude Opus 5.57.81s

Simulador Interactivo de Costos de Tokens

Adjust sliders to calculate projected monthly cloud API spending with prompt caching discounts.

100K / day
10K500K1M
30K / day
5K150K300K
50% cached
0% (Cold)50%90% (Warm)
Gemini 3.5 Flash-Lite Monthly API Bill
$2.55 / month
$0.4/1M in · $2/1M out
Claude Opus 5.5 Monthly API Bill
$24.60 / month
$4/1M in · $20/1M out
Gemini 3.5 Flash-Lite saves approximately $22.05 / month (90%) under this workload.9.6x Savings

Gemini 3.5 Flash-Lite · Strengths & Key Advantages

  • Economical alto-volumen precios
  • rápido ejecución con bajo latencia
  • 1M ventana de contexto
Considerations & Trade-offs
  • menos adept un profundo matemático proofs
  • texto y visión solo (no audio entrada)

Claude Opus 5.5 · Strengths & Key Advantages

  • inigualable autónomo multipaso codificación agéntica y refactorización
  • Flawless multidocumento razonamiento sobre 1M token contexto con no largo-prompt surcharge
  • más alto Artificial análisis inteligencia Index ever measured (58/100), leading SWE-bench Pro un 89.9%
  • Precise herramienta ejecución con robust error-recovery bucles
Considerations & Trade-offs
  • Premium salida precios ($20.00/M) plus heavy razonamiento-token usage (~119K salida tokens por index tarea) keeps largo agentic se ejecuta expensive
  • largo pensamiento times un maximum effort puede push un único hard agentic tarea toward ~$13

Token Use & Task Economics

Estimated cost and token consumption to complete one representative agentic task.

MetricGemini 3.5 Flash-LiteClaude Opus 5.5
Cost per Task
Estimated USD to finish one task
$0.55$3.85
Output Tokens / Task
Tokens generated per task
18K68K
Reasoning Tokens / Task
Thinking tokens burned per task
7K42K

Especificaciones Técnicas

Architecture, release timeline, capabilities, and modalities.

CaracterísticaGemini 3.5 Flash-LiteClaude Opus 5.5
Official API Model ID (SDK)models/gemini-3-5-flash-liteclaude-opus-5-5-20260901
API ProtocolGoogle Gemini API (generateContent) + OpenAI-Compatible SDKAnthropic Messages API + AWS Bedrock + Vertex AI
Release Date2026-072026-09
Knowledge CutoffMarch 2026June 2026
ArchitectureLightweight efficient multimodal transformerExtended Autonomous Hybrid Reasoning Transformer
ParametersConfidentialConfidential
Enterprise Rate Limits (RPM / TPM)10,000 – 30,000 RPM (Tier 5 Enterprise) · 4,000,000 – 10,000,000 TPM4,000 – 10,000 RPM (Tier 4 + Priority Tier) · 4,000,000 – 10,000,000 TPM
Long-Context Tier (>128K/200K)Standard rate up to 200K tokens; 2x input/output tier above 200K tokensStandard rate up to 200K tokens; 2x input/output tier above 200K tokens
Prompt Cache Policy & TTLImplicit + Explicit Context Caching at $0.1/1M with configurable TTL$0.4/1M cached read (90% off); 5-min default & 1-hour extended TTL
Computer Use / GUI AgentSupported (Screen coordinate grounding, browser & desktop GUI action loops)Supported (Screen coordinate grounding, browser & desktop GUI action loops)
MCP & Parallel Tool CallingNative MCP Client/Server ready · Parallel function calling · 100% Strict JSON SchemaNative MCP Client/Server ready · Parallel function calling · 100% Strict JSON Schema
Web Search Grounding & SandboxBuilt-in Google Search Grounding + Managed Python Code Execution SandboxNative Web Search Citations + Code Execution Tool + MCP Connectors
Official Fine-Tuning SupportVertex AI Supervised Adapter Tuning & Context DistillationCustom Model Tuning via Amazon Bedrock & Prompt Distillation
Multimodal Token BillingText 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 VRAMCloud-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 ClassificationGoogle DeepMind Frontier Safety Framework VerifiedAnthropic RSP ASL-2 / ASL-3 Constitutional Safety Evaluated
Enterprise Compliance (ZDR/HIPAA/SOC2)SOC2 Type II · GDPR · Zero Data Retention (ZDR) & HIPAA BAA Available on Enterprise TierSOC2 Type II · GDPR · Zero Data Retention (ZDR) & HIPAA BAA Available on Enterprise Tier
Input Modalitiestext, visiontext, vision
Output Modalitiestexttext

Gemini 3.5 Flash-Lite vs Claude Opus 5.5: Respuestas Rápidas (FAQ)

Which is more intelligent, Gemini 3.5 Flash-Lite or Claude Opus 5.5?

Claude Opus 5.5 is more intelligent. It scores 94 compared with 69 on the composite Intelligence Index.

Which model is faster?

Gemini 3.5 Flash-Lite is faster, generating 240 tokens per second compared with 78 tokens per second.

Which model is cheaper to run?

Gemini 3.5 Flash-Lite is cheaper at $0.35 per 1M tokens blended, compared with $3.08 (7:2:1 cache:input:output ratio).

Which model has lower latency?

Gemini 3.5 Flash-Lite has lower latency, with a time to first token of 0.29s compared with 1.40s.

Which model has a larger context window?

Gemini 3.5 Flash-Lite has a larger context window, supporting 1.05M tokens compared with 1M tokens.

Veredicto

Choose Gemini 3.5 Flash-Lite for autónomo subagent bucles, resumen, y datos transformation, or choose Claude Opus 5.5 for grande-escala software development agentes, empresarial codebases, y de misión crítica científico síntesis.

Preguntas frecuentes

Which is cheaper: Gemini 3.5 Flash-Lite or Claude Opus 5.5?+

Gemini 3.5 Flash-Lite costs $0.4/1M input and $2/1M output tokens, while Claude Opus 5.5 costs $4/1M input and $20/1M output tokens.

Which model has a larger context window: Gemini 3.5 Flash-Lite or Claude Opus 5.5?+

Gemini 3.5 Flash-Lite supports 1.0M tokens whereas Claude Opus 5.5 supports 1M tokens.

AI Terminology & Guide
Explore Glossary →

Understand key metrics like context window, token pricing, reasoning mode, and SWE-bench scores in our comprehensive AI glossary.

Las puntuaciones de benchmarks, precios por token y límites de tasa evolucionan rápidamente. Verifique siempre las especificaciones actuales en la documentación oficial del proveedor.

Fuentes: Google & Anthropic Official Documentation