Gemma 4 31B vs Llama 5
Benchmarks, API Token Pricing, Context Window & Speed Comparison
Related topics (4)
Compare Gemma 4 31B by Google against Llama 5 by Meta across real-world coding, reasoning, token economics, and latency.
원하는 AI 모델 선택 및 실시간 비교
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Live comparison: no in-depth editorial review has been published for this pair yet.
Gemma 4 31B
오픈 웨이트 추론 모델Google의 Gemma 4 31B 모델은 강화학습 기반의 고효율 오픈 웨이트 사고 사슬(Chain-of-Thought) 추론에 최적화된 AI 모델로, 262K 컨텍스트 윈도우, 초당 80 토큰의 생성 속도, 종합 지능 지수 81/100점(무료 오픈 웨이트 제공)을 제공합니다.
$0.00 / 1M
$0.00 / 1M
262.1K tokens
68.5%
Llama 5
오픈 웨이트 파운데이션 모델Meta의 Llama 5 모델은 프라이빗 클라우드 자체 호스팅, 파인튜닝 및 완벽한 데이터 주권 확보에 최적화된 AI 모델로, 5M 컨텍스트 윈도우, 초당 65 토큰의 생성 속도, 종합 지능 지수 94/100점(무료 오픈 웨이트 제공)을 제공합니다.
$0.00 / 1M
$0.00 / 1M
5M tokens
74%
한눈에 보는 핵심 차이점
- Both models cost the same under this workload — choose on capability, not price.
- Llama 5 has a larger context window (5M vs 262K tokens).
- Gemma 4 31B is faster at 80 tok/s vs 65 tok/s.
- Llama 5 leads on SWE-bench coding (74.0% vs 68.5%).
| Feature | Gemma 4 31B | Llama 5 |
|---|---|---|
Intelligence Index Composite reasoning & knowledge score (0-100) | 81 | 94 |
Provider | Meta | |
Input Price Cost per million input tokens | $0.00 / 1M | $0.00 / 1M |
Output Price Cost per million output tokens | $0.00 / 1M | $0.00 / 1M |
Blended Price Weighted 7:2:1 cache:input:output per 1M | $0.00 / 1M | $0.00 / 1M |
Context Window Maximum input + output tokens | 262.1K tokens~393 A4 pages of text | 5M tokens~7,500 A4 pages of text |
Max Output Tokens Per single request | 33K | 128K |
Throughput Speed Tokens generated per second | 80 tok/s | 65 tok/s |
SWE-bench Verified Real-world software engineering | 68.5% | 74% |
GPQA Diamond Graduate-level reasoning | 77% | 84% |
부문별 우위 모델
At-a-glance decision framework검증된 벤치마크 점수 비교
Official evaluation metrics verified by independent third-party research labs.
Industry benchmark evaluating real-world GitHub issue resolution and multi-file debugging.
Challenging questions created by domain experts to test complex PhD-level reasoning.
Hardened reasoning and academic knowledge benchmark with reduced lucky-guess probability.
Crowdsourced human preference evaluation based on blind, head-to-head model battles.
Contamination-free competitive programming problems collected continuously over time.
American Invitational Mathematics Examination — multi-step Olympiad-level reasoning.
Tokens emitted per second during active generation streaming.
지연 시간(Latency) 및 응답 속도
Time to first token (TTFT) and estimated end-to-end response time for a typical 500-token response.
실시간 API 토큰 비용 시뮬레이터
Adjust sliders to calculate projected monthly cloud API spending with prompt caching discounts.
Gemma 4 31B · Strengths & Key Advantages
- 262K 컨텍스트 윈도우 및 최대 33K 출력 토큰 생성 지원 (SWE-bench Verified 68.5% · GPQA Diamond 77%)
- 초당 80 토큰의 빠른 처리 속도와 480ms에 불과한 첫 토큰 지연 시간(TTFT)
- 오픈 웨이트 아키텍처 (31B)를 통해 vLLM, SGLang, Ollama 기반 자체 GPU 서버 구축 및 파인튜닝 가능
Considerations & Trade-offs
- 비양자화 전체 정밀도로 로컬 서버에서 구동하려면 충분한 GPU VRAM 용량이 필요합니다
- 초고난도 학술 연구 및 수학 증명 과제에서는 상위 대형 추론 특화 모델이 더 깊은 분석을 제공할 수 있습니다
Llama 5 · Strengths & Key Advantages
- 5M 컨텍스트 윈도우 및 최대 128K 출력 토큰 생성 지원 (SWE-bench Verified 74% · GPQA Diamond 84%)
- 초당 65 토큰의 빠른 처리 속도와 850ms에 불과한 첫 토큰 지연 시간(TTFT)
- 오픈 웨이트 아키텍처 (600B+ total (~40-80B ))를 통해 vLLM, SGLang, Ollama 기반 자체 GPU 서버 구축 및 파인튜닝 가능
Considerations & Trade-offs
- 비양자화 전체 정밀도로 로컬 서버에서 구동하려면 충분한 GPU VRAM 용량이 필요합니다
- 초고난도 학술 연구 및 수학 증명 과제에서는 상위 대형 추론 특화 모델이 더 깊은 분석을 제공할 수 있습니다
Token Use & Task Economics
Estimated cost and token consumption to complete one representative agentic task.
| Metric | Gemma 4 31B | Llama 5 |
|---|---|---|
Cost per Task Estimated USD to finish one task | $0.00 | $0.00 |
Output Tokens / Task Tokens generated per task | 12K | 40K |
Reasoning Tokens / Task Thinking tokens burned per task | 6K | 20K |
기술 사양 (Specifications)
Architecture, release timeline, capabilities, and modalities.
| Feature | Gemma 4 31B | Llama 5 |
|---|---|---|
| Official API Model ID (SDK) | models/gemma-4-31b | meta-llama/Llama-5 |
| API Protocol | Google Gemini API (generateContent) + OpenAI-Compatible SDK | OpenAI-Compatible REST / vLLM / SGLang / HuggingFace TGI |
| Release Date | 2026-04 | 2026-04 |
| Knowledge Cutoff | January 2026 | January 2026 |
| Architecture | Dense open reasoning transformer with native thinking | Mixture-of-Experts with recursive self-improvement (~600B total, 40-80B active) |
| Parameters | 31B | 600B+ total (~40-80B active) |
| Enterprise Rate Limits (RPM / TPM) | Unlimited (Hardware-bound on self-hosted GPU) / 10,000 RPM Cloud · Unlimited local throughput / 10M+ TPM Managed Cloud | Unlimited (Hardware-bound on self-hosted GPU) / 10,000 RPM Cloud · Unlimited local throughput / 10M+ TPM Managed Cloud |
| Long-Context Tier (>128K/200K) | Free across full context window (only KV-cache VRAM scales) | Free across full context window (only KV-cache VRAM scales) |
| Prompt Cache Policy & TTL | Automatic prefix KV-caching in vLLM / SGLang (0 cost) | Automatic prefix KV-caching in vLLM / SGLang (0 cost) |
| 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 | Supported via external Tavily/Exa Search & E2B/Docker Sandbox tools |
| Official Fine-Tuning Support | Full Weights SFT, LoRA, QLoRA, DPO & GRPO RL (Unsloth / Axolotl / Llama-Factory) | Full Weights SFT, LoRA, QLoRA, DPO & GRPO RL (Unsloth / Axolotl / Llama-Factory) |
| 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) · Audio (~25–32 tokens/sec) · Video (~258–300 tokens/sec) |
| Quantization & Min GPU VRAM | BF16 / FP16, FP8 Dynamic, INT4 AWQ / GPTQ, GGUF (Q4_K_M / Q8_0) · 19 GB VRAM (4-bit Quantized) · 67 GB VRAM (Native BF16) | 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 Classification | Google DeepMind Frontier Safety Framework Verified | Open-Weights System Card + Customizable Guardrail Layer (Llama Guard / ShieldGemma) |
| Enterprise Compliance (ZDR/HIPAA/SOC2) | 100% Air-Gapped On-Premises / Private VPC · Full HIPAA, GDPR & SOC2 Control | 100% Air-Gapped On-Premises / Private VPC · Full HIPAA, GDPR & SOC2 Control |
| Input Modalities | text, vision | text, vision, video, audio |
| Output Modalities | text | text |
Gemma 4 31B vs Llama 5: 핵심 요약 문답 (FAQ)
Which is more intelligent, Gemma 4 31B or Llama 5?
Llama 5 is more intelligent. It scores 94 compared with 81 on the composite Intelligence Index.
Which model is faster?
Gemma 4 31B is faster, generating 80 tokens per second compared with 65 tokens per second.
Which model has lower latency?
Gemma 4 31B has lower latency, with a time to first token of 0.48s compared with 0.85s.
Which model has a larger context window?
Llama 5 has a larger context window, supporting 5M tokens compared with 262.1K tokens.
종합 평가
Choose Gemma 4 31B for 강화학습 기반의 고효율 오픈 웨이트 사고 사슬(chain-of-thought) 추론, 엔터프라이즈 rag 지식베이스 및 대규모 프로덕션 api 연동에 최적., or choose Llama 5 for 프라이빗 클라우드 자체 호스팅, 파인튜닝 및 완벽한 데이터 주권 확보, 엔터프라이즈 rag 지식베이스 및 대규모 프로덕션 api 연동에 최적..
자주 묻는 질문 (FAQ)
Which is cheaper: Gemma 4 31B or Llama 5?+
Gemma 4 31B costs $0/1M input and $0/1M output tokens, while Llama 5 costs $0/1M input and $0/1M output tokens.
Which model has a larger context window: Gemma 4 31B or Llama 5?+
Gemma 4 31B supports 262K tokens whereas Llama 5 supports 5M tokens.
Understand key metrics like context window, token pricing, reasoning mode, and SWE-bench scores in our comprehensive AI glossary.
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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.
공식 출처: Google & Meta Official Documentation