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Head-to-Head Model Comparison

Gemma 4 31B vs Llama 3.3 70B

Benchmarks, API Token Pricing, Context Window & Speed Comparison

由 GetInfoUs 研究团队审核最后审核日期：October 2026编辑政策
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AI CompareAI BenchmarkToken PricingModel CostModel SpecsSWE-bench CodingModel VersusWhich AI ModelLLM Speed & LatencyReasoning ModelsExtended ThinkingPrompt Caching ROI
Gemma 4 31B vs Llama 3.3 70B: Benchmarks, Pricing & Speed Comparison

Google 的 Gemma 4 31B 与 Meta 的 Llama 3.3 70B 是当前备受关注的两款主力 AI 模型。Gemma 4 31B 提供 262.1K Token 上下文窗口，API 定价为每百万 Token 输入 $0.00 / 输出 $0.00；而 Llama 3.3 70B 提供 128K Token 上下文窗口，定价为每百万 Token 输入 $0.00 / 输出 $0.00。以下为两者的基准测试、延迟与成本全面对比。

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Google

Gemma 4 31B

开源权重推理大模型

Google 推出的 Gemma 4 31B 是一款通过大规模强化学习训练、支持私有化部署的开源权重推理大模型，具备 262K 上下文窗口、80 Token/秒生成速度与 81/100 综合智能指数（免费开源权重）。

Intelligence Index81 / 100
Input Token:

$0.00 / 1M

Output Token:

$0.00 / 1M

Context Window:

262.1K tokens

SWE-bench Verified:

68.5%

Meta

Llama 3.3 70B

开源权重基础大模型

Meta 推出的 Llama 3.3 70B 是一款支持私有云服务器自托管、微调与完全数据主权的开源权重基础大模型，具备 128K 上下文窗口、280 Token/秒生成速度与 38/100 综合智能指数（免费开源权重）。

Intelligence Index38 / 100
Input Token:

$0.00 / 1M

Output Token:

$0.00 / 1M

Context Window:

128K tokens

SWE-bench Verified:

39.5%

核心差异一览

  • Gemma 4 31B is 1.0x cheaper for API usage (input + output tokens combined).
  • Gemma 4 31B has a larger context window (262K vs 128K tokens).
  • Llama 3.3 70B is faster at 280 tok/s vs 80 tok/s.
  • Gemma 4 31B leads on SWE-bench coding (68.5% vs 39.5%).
Need definitions for token pricing, context, or benchmarks?Explore AI Glossary →
FeatureGemma 4 31BLlama 3.3 70B
Intelligence Index
Composite reasoning & knowledge score (0-100)
8138
Provider
GoogleMeta
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$0.07 / 1M
Context Window
Maximum input + output tokens
262.1K tokens~393 A4 pages of text128K tokens~192 A4 pages of text
Max Output Tokens
Per single request
33K4K
Throughput Speed
Tokens generated per second
80 tok/s280 tok/s
SWE-bench Verified
Real-world software engineering
68.5%39.5%
GPQA Diamond
Graduate-level reasoning
77%48.9%
Cached Input Price
Prompt caching discount per 1M tokens
$0.00 / 1M$0.10 / 1M

各项指标谁更胜一筹？

At-a-glance decision framework
Coding
Gemma 4 31B
+29.0% SWE-bench lead
Cost
Gemma 4 31B
1.0x cheaper monthly runtime
Speed
Llama 3.3 70B
+250% faster generation
Context
Gemma 4 31B
262.1K tokens window

权威基准测试得分对比

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

Gemma 4 31BLlama 3.3 70B
SWE-bench Verified (Autonomous Coding)

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

68.5%vs39.5%
GPQA Diamond (Doctorate-Level Science & Math)

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

77%vs48.9%
MMLU-Pro (Multi-task Language Understanding)

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

84.8%vs71%
LMSYS Chatbot Arena Elo

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

1410ptsvs1285pts
Humanity's Last Exam (Frontier Expert Knowledge)

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

N/Avs7.8%
Terminal-Bench (Agentic Coding & Terminal Use)

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

N/Avs26.1%
SciCode (Scientific Computing)

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

N/Avs23.7%
LiveCodeBench (Competitive Coding)

Contamination-free competitive programming problems collected continuously over time.

N/Avs42.5%
AIME (Advanced Math Competition)

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

86%vs56.2%
Long-Context Reasoning

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

N/Avs63.9%
Generation Speed (Throughput)

Tokens emitted per second during active generation streaming.

80 tok/s vs280 tok/s

延迟与响应速度

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
Gemma 4 31B0.48s
Llama 3.3 70B0.25s
Est. End-to-End Response (500 tokens)TTFT + generation time
Gemma 4 31B6.73s
Llama 3.3 70B2.04s

实时 Token 成本模拟器

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)
Gemma 4 31B Monthly API Bill
$0.00 / month
$0/1M in · $0/1M out
Llama 3.3 70B Monthly API Bill
$0.15 / month
$0/1M in · $0/1M out
Gemma 4 31B saves approximately $0.15 / month (100%) under this workload.1.0x Savings

Gemma 4 31B · Strengths & Key Advantages

  • 提供 262K 上下文窗口与最高 33K 输出容量，核心基准表现优异（SWE-bench Verified 68.5% · GPQA Diamond 77%）
  • 高达 80 Token/秒的生成吞吐量，首字延迟 (TTFT) 仅约 480 毫秒
  • 开放模型权重 (31B)，支持通过 vLLM、SGLang 或 Ollama 在私有 GPU 集群上本地部署与微调
Considerations & Trade-offs
  • 在本地以满血精度运行需要较高的 GPU 显存 (VRAM) 与运维基础设施
  • 在超长链路的前沿科研推导任务上，深度略逊于顶配重型旗舰推理模型

Llama 3.3 70B · Strengths & Key Advantages

  • 提供 128K 上下文窗口与最高 4K 输出容量，核心基准表现优异（SWE-bench Verified 39.5% · GPQA Diamond 48.9%）
  • 高达 280 Token/秒的生成吞吐量，首字延迟 (TTFT) 仅约 250 毫秒
  • 开放模型权重 (70B)，支持通过 vLLM、SGLang 或 Ollama 在私有 GPU 集群上本地部署与微调
Considerations & Trade-offs
  • 在本地以满血精度运行需要较高的 GPU 显存 (VRAM) 与运维基础设施
  • 在超长链路的前沿科研推导任务上，深度略逊于顶配重型旗舰推理模型

Token Use & Task Economics

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

MetricGemma 4 31BLlama 3.3 70B
Cost per Task
Estimated USD to finish one task
$0.00$0.21
Output Tokens / Task
Tokens generated per task
12K45K
Reasoning Tokens / Task
Thinking tokens burned per task
6K0K

技术规格参数

Architecture, release timeline, capabilities, and modalities.

FeatureGemma 4 31BLlama 3.3 70B
Official API Model ID (SDK)models/gemma-4-31bmeta-llama/Llama-3.3-70B
API ProtocolGoogle Gemini API (generateContent) + OpenAI-Compatible SDKOpenAI-Compatible REST / vLLM / SGLang / HuggingFace TGI
Release Date2026-042024-12
Knowledge CutoffJanuary 2026December 2023
ArchitectureDense open reasoning transformer with native thinkingDense Autoregressive Transformer
Parameters31B70B
Enterprise Rate Limits (RPM / TPM)Unlimited (Hardware-bound on self-hosted GPU) / 10,000 RPM Cloud · Unlimited local throughput / 10M+ TPM Managed CloudUnlimited (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 & TTLAutomatic prefix KV-caching in vLLM / SGLang (0 cost)Automatic prefix KV-caching in vLLM / SGLang (0 cost)
Computer Use / GUI AgentSupported (Screen coordinate grounding, browser & desktop GUI action loops)API / CLI Tool-Use & DOM Agent ready (no raw pixel desktop click loop)
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 SupportFull 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 BillingText BPE (~4 chars/token) · Vision (~258–765 tokens per image tile)Text BPE (~4 chars/token)
Quantization & Min GPU VRAMBF16 / 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) · 43 GB VRAM (4-bit Quantized) · 151 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)100% Air-Gapped On-Premises / Private VPC · Full HIPAA, GDPR & SOC2 Control100% Air-Gapped On-Premises / Private VPC · Full HIPAA, GDPR & SOC2 Control
Input Modalitiestext, visiontext
Output Modalitiestexttext

Gemma 4 31B vs Llama 3.3 70B: 常见问题解答 (FAQ)

Which is more intelligent, Gemma 4 31B or Llama 3.3 70B?

Gemma 4 31B is more intelligent. It scores 81 compared with 38 on the composite Intelligence Index.

Which model is faster?

Llama 3.3 70B is faster, generating 280 tokens per second compared with 80 tokens per second.

Which model is cheaper to run?

Gemma 4 31B is cheaper at $0.00 per 1M tokens blended, compared with $0.07 (7:2:1 cache:input:output ratio).

Which model has lower latency?

Llama 3.3 70B has lower latency, with a time to first token of 0.25s compared with 0.48s.

Which model has a larger context window?

Gemma 4 31B has a larger context window, supporting 262.1K tokens compared with 128K tokens.

综合结论

综合来看，Gemma 4 31B（适合：通过大规模强化学习训练、支持私有化部署的开源权重推理任务、企业级 RAG 检索增强生成、自动化智能体工作流与高可靠 API 集成。）与 Llama 3.3 70B（适合：支持私有云服务器自托管、微调与完全数据主权的开源权重基础任务、企业级 RAG 检索增强生成、自动化智能体工作流与高可靠 API 集成。）各具优势：Gemma 4 31B 在综合智能与编程基准测试中表现更强，Llama 3.3 70B 拥有更快的 Token 生成速度，而 Gemma 4 31B 则在规模化 API 部署中具备更高的成本效益。

常见问题 (FAQ)

在编程和深度推理方面，Gemma 4 31B 和 Llama 3.3 70B 哪个更强？+

在综合智能指数与编程基准评测中，Gemma 4 31B 表现占优。建议结合上方的 SWE-bench Verified 与 GPQA Diamond 跑分根据具体场景选择。

Gemma 4 31B 与 Llama 3.3 70B 的 API 价格哪个更便宜？+

Gemma 4 31B 的 API 价格为输入 $0.00 / 输出 $0.00（每百万 Token），Llama 3.3 70B 为输入 $0.00 / 输出 $0.00。综合混合成本来看，Gemma 4 31B 更具性价比。

哪款模型拥有更大的上下文窗口和更快的响应速度？+

Gemma 4 31B 支持 262.1K Token 上下文窗口，而 Llama 3.3 70B 支持 128K Token。在流式生成速度方面，Llama 3.3 70B 的吞吐表现更快。

AI Terminology & Guide
Explore Glossary →

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.

数据来源: Google 与 Meta 官方 API 文档、模型发布说明及独立基准测试报告 (2026)。