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Head-to-Head Model Comparison

Gemini 3.5 Flash-Lite vs Llama 5

Benchmarks, API Token Pricing, Context Window & Speed Comparison

GetInfoUs अनुसंधान दल द्वारा समीक्षितअंतिम समीक्षा: October 2026संपादकीय नीति
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Compare Gemini 3.5 Flash-Lite by Google against Llama 5 by Meta across real-world coding, reasoning, token economics, and latency.

आमने-सामने मॉडल तुलना (VS)

किसी भी मॉडल को बदलें और तुलना करें

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Live comparison: no in-depth editorial review has been published for this pair yet.

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Google

Gemini 3.5 Flash-Lite

अल्ट्रा-फ़ास्ट लो-लेटेंसी

Google का Gemini 3.5 Flash-Lite रियल-टाइम चैट, अल्ट्रा-लो लेटेंसी और हाई-वॉल्यूम राउटिंग के लिए निर्मित एक उन्नत AI मॉडल है, जो 1.0M कॉन्टेक्स्ट विंडो, 240 टोकन/सेकंड स्पीड और $0.4/$2 प्रति 1M टोकन कीमत के साथ 69/100 इंटेलीजेंस स्कोर प्रदान

Intelligence Index69 / 100
Input Token:

$0.40 / 1M

Output Token:

$2.00 / 1M

Context Window:

1.05M tokens

SWE-bench Verified:

N/A

Meta

Llama 5

ओपन-वेट्स फाउंडेशन मॉडल

Meta का Llama 5 प्राइवेट क्लाउड सेल्फ-होस्टिंग, फ़ाइन-ट्यूनिंग और पूर्ण डेटा गोपनीयता के लिए निर्मित एक उन्नत AI मॉडल है, जो 5M कॉन्टेक्स्ट विंडो, 65 टोकन/सेकंड स्पीड और निःशुल्क ओपन-वेट्स एक्सेस के साथ 94/100 इंटेलीजेंस स्कोर प्रदान करता ह

Intelligence Index94 / 100
Input Token:

$0.00 / 1M

Output Token:

$0.00 / 1M

Context Window:

5M tokens

SWE-bench Verified:

74%

एक नज़र में मुख्य अंतर

  • Llama 5 is 1.0x cheaper for API usage (input + output tokens combined).
  • Llama 5 has a larger context window (5M vs 1M tokens).
  • Gemini 3.5 Flash-Lite is faster at 240 tok/s vs 65 tok/s.
Need definitions for token pricing, context, or benchmarks?Explore AI Glossary →
FeatureGemini 3.5 Flash-LiteLlama 5
Intelligence Index
Composite reasoning & knowledge score (0-100)
6994
Provider
GoogleMeta
Input Price
Cost per million input tokens
$0.40 / 1M$0.00 / 1M
Output Price
Cost per million output tokens
$2.00 / 1M$0.00 / 1M
Blended Price
Weighted 7:2:1 cache:input:output per 1M
$0.35 / 1M$0.00 / 1M
Context Window
Maximum input + output tokens
1.05M tokens~1,573 A4 pages of text5M tokens~7,500 A4 pages of text
Max Output Tokens
Per single request
33K128K
Throughput Speed
Tokens generated per second
240 tok/s65 tok/s
SWE-bench Verified
Real-world software engineering
N/A74%
GPQA Diamond
Graduate-level reasoning
N/A84%
Cached Input Price
Prompt caching discount per 1M tokens
$0.10 / 1M$0.00 / 1M

किस क्षेत्र में कौन आगे है?

At-a-glance decision framework
Coding

Evaluations ongoing

Cost
Llama 5
1.0x cheaper monthly runtime
Speed
Gemini 3.5 Flash-Lite
+269% faster generation
Context
Llama 5
5M tokens window

सत्यापित बेंचमार्क स्कोर तुलना

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

Gemini 3.5 Flash-LiteLlama 5
SWE-bench Verified (Autonomous Coding)

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

N/Avs74%
GPQA Diamond (Doctorate-Level Science & Math)

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

N/Avs84%
MMLU-Pro (Multi-task Language Understanding)

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

81.2%vs87%
LMSYS Chatbot Arena Elo

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

1375ptsvs1485pts
LiveCodeBench (Competitive Coding)

Contamination-free competitive programming problems collected continuously over time.

N/Avs68%
AIME (Advanced Math Competition)

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

N/Avs88%
Generation Speed (Throughput)

Tokens emitted per second during active generation streaming.

240 tok/s vs65 tok/s

लेटेंसी और रिस्पॉन्स समय

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
Llama 50.85s
Est. End-to-End Response (500 tokens)TTFT + generation time
Gemini 3.5 Flash-Lite2.37s
Llama 58.54s

रियल-टाइम टोकन लागत सिम्युलेटर

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
Llama 5 Monthly API Bill
$0.00 / month
$0/1M in · $0/1M out
Llama 5 saves approximately $2.55 / month (100%) under this workload.1.0x Savings

Gemini 3.5 Flash-Lite · Strengths & Key Advantages

  • 1.0M कॉन्टेक्स्ट विंडो और अधिकतम 33K आउटपुट टोकन क्षमता (MMLU-Pro 81.2% · Arena Elo 1375)
  • 240 टोकन/सेकंड की तेज़ जनरेशन स्पीड और मात्र 290ms का प्रथम टोकन लेटेंसी समय (TTFT)
  • $0.4/$2 प्रति 1M टोकन कीमत पर प्रॉम्प्ट कैशिंग, फंक्शन कॉलिंग और स्ट्रक्चर्ड JSON आउटपुट का पूर्ण समर्थन
Considerations & Trade-offs
  • प्रोप्राइटरी क्लाउड मॉडल — इसके वेट्स को ऑफ़लाइन लोकल सर्वर पर डाउनलोड नहीं किया जा सकता
  • अत्यंत जटिल वैज्ञानिक शोध कार्यों में शीर्ष फ्लैगशिप रीजनिंग मॉडल की तुलना में गहराई थोड़ी कम हो सकती है

Llama 5 · Strengths & Key Advantages

  • 5M कॉन्टेक्स्ट विंडो और अधिकतम 128K आउटपुट टोकन क्षमता (SWE-bench Verified 74% · GPQA Diamond 84%)
  • 65 टोकन/सेकंड की तेज़ जनरेशन स्पीड और मात्र 850ms का प्रथम टोकन लेटेंसी समय (TTFT)
  • ओपन-वेट्स आर्किटेक्चर (600B+ total (~40-80B )) — vLLM, Ollama या निजी GPU क्लस्टर पर सेल्फ-होस्टिंग के लिए उपयुक्त
Considerations & Trade-offs
  • पूर्ण प्रिसिजन में लोकल GPU सर्वर पर चलाने के लिए पर्याप्त VRAM और हार्डवेयर संसाधनों की आवश्यकता होती है
  • अत्यंत जटिल वैज्ञानिक शोध कार्यों में शीर्ष फ्लैगशिप रीजनिंग मॉडल की तुलना में गहराई थोड़ी कम हो सकती है

Token Use & Task Economics

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

MetricGemini 3.5 Flash-LiteLlama 5
Cost per Task
Estimated USD to finish one task
$0.55$0.00
Output Tokens / Task
Tokens generated per task
18K40K
Reasoning Tokens / Task
Thinking tokens burned per task
7K20K

तकनीकी विशिष्टताएँ (Specs)

Architecture, release timeline, capabilities, and modalities.

FeatureGemini 3.5 Flash-LiteLlama 5
Official API Model ID (SDK)models/gemini-3-5-flash-litemeta-llama/Llama-5
API ProtocolGoogle Gemini API (generateContent) + OpenAI-Compatible SDKOpenAI-Compatible REST / vLLM / SGLang / HuggingFace TGI
Release Date2026-072026-04
Knowledge CutoffMarch 2026January 2026
ArchitectureLightweight efficient multimodal transformerMixture-of-Experts with recursive self-improvement (~600B total, 40-80B active)
ParametersConfidential600B+ total (~40-80B active)
Enterprise Rate Limits (RPM / TPM)10,000 – 30,000 RPM (Tier 5 Enterprise) · 4,000,000 – 10,000,000 TPMUnlimited (Hardware-bound on self-hosted GPU) / 10,000 RPM Cloud · Unlimited local throughput / 10M+ TPM Managed Cloud
Long-Context Tier (>128K/200K)Standard rate up to 200K tokens; 2x input/output tier above 200K tokensFree across full context window (only KV-cache VRAM scales)
Prompt Cache Policy & TTLImplicit + Explicit Context Caching at $0.1/1M with configurable TTLAutomatic prefix KV-caching in vLLM / SGLang (0 cost)
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 SandboxSupported via external Tavily/Exa Search & E2B/Docker Sandbox tools
Official Fine-Tuning SupportVertex AI Supervised Adapter Tuning & Context DistillationFull Weights SFT, LoRA, QLoRA, DPO & GRPO RL (Unsloth / Axolotl / Llama-Factory)
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) · Audio (~25–32 tokens/sec) · Video (~258–300 tokens/sec)
Quantization & Min GPU VRAMCloud-Hosted Proprietary (FP8 / BF16 Tensor Core Cluster) · Zero Local VRAM Required (100% Serverless Cloud API)BF16 / FP16, FP8 Dynamic, INT4 AWQ / GPTQ, GGUF (Q4_K_M / Q8_0) · 372 GB VRAM (4-bit Quantized) · 1290 GB VRAM (Native BF16)
Safety & Frontier ClassificationGoogle DeepMind Frontier Safety Framework VerifiedOpen-Weights System Card + Customizable Guardrail Layer (Llama Guard / ShieldGemma)
Enterprise Compliance (ZDR/HIPAA/SOC2)SOC2 Type II · GDPR · Zero Data Retention (ZDR) & HIPAA BAA Available on Enterprise Tier100% Air-Gapped On-Premises / Private VPC · Full HIPAA, GDPR & SOC2 Control
Input Modalitiestext, visiontext, vision, video, audio
Output Modalitiestexttext

Gemini 3.5 Flash-Lite vs Llama 5: त्वरित प्रश्नोत्तर (FAQ)

Which is more intelligent, Gemini 3.5 Flash-Lite or Llama 5?

Llama 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 65 tokens per second.

Which model is cheaper to run?

Llama 5 is cheaper at $0.00 per 1M tokens blended, compared with $0.35 (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 0.85s.

Which model has a larger context window?

Llama 5 has a larger context window, supporting 5M tokens compared with 1.05M tokens.

निष्कर्ष

Choose Gemini 3.5 Flash-Lite for रियल-टाइम चैट, अल्ट्रा-लो लेटेंसी और हाई-वॉल्यूम राउटिंग, एंटरप्राइज rag सिस्टम और स्केलेबल api इंटीग्रेशन के लिए सर्वोत्तम।, or choose Llama 5 for प्राइवेट क्लाउड सेल्फ-होस्टिंग, फ़ाइन-ट्यूनिंग और पूर्ण डेटा गोपनीयता, एंटरप्राइज rag सिस्टम और स्केलेबल api इंटीग्रेशन के लिए सर्वोत्तम।.

अक्सर पूछे जाने वाले प्रश्न (FAQ)

Which is cheaper: Gemini 3.5 Flash-Lite or Llama 5?+

Gemini 3.5 Flash-Lite costs $0.4/1M input and $2/1M output tokens, while Llama 5 costs $0/1M input and $0/1M output tokens.

Which model has a larger context window: Gemini 3.5 Flash-Lite or Llama 5?+

Gemini 3.5 Flash-Lite supports 1.0M tokens whereas Llama 5 supports 5M tokens.

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

Tokens & PricingContext WindowAI BenchmarksReasoning ModelsTool CallingModel Context Protocol (MCP)

Related reading

Benchmark scores, token prices, and rate limits evolve rapidly as AI labs release model updates. Always verify current specifications and pricing on the official provider documentation.

स्रोत: Google & Meta Official Documentation