DeepSeek V4 vs Gemma 4 E2B
Benchmarks, API Token Pricing, Context Window & Speed Comparison
Related topics (12)
DeepSeek V4 by DeepSeek and Gemma 4 E2B 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, Gemma 4 E2B holds a notable pricing advantage for token ingestion. On output generation, Gemma 4 E2B is more economical per million generated tokens. In data-heavy architectures requiring extensive repository or document analysis, DeepSeek V4 provides an expanded memory horizon with 1,000K tokens. In latency-sensitive production environments, Gemma 4 E2B 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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DeepSeek V4
开源权重模型DeepSeek 推出的 DeepSeek V4 人工智能模型，具备 1M 上下文窗口，输入价格为 $0.3/百万 Token，输出价格为 $1.2/百万 Token，专为高质量生产环境、推理与自动化工作流打造。
$0.30 / 1M
$1.20 / 1M
1M tokens
62.8%
Gemma 4 E2B
开源权重模型Google 推出的 Gemma 4 E2B 人工智能模型，具备 131K 上下文窗口，输入价格为 $0/百万 Token，输出价格为 $0/百万 Token，专为高质量生产环境、推理与自动化工作流打造。
$0.00 / 1M
$0.00 / 1M
131.1K tokens
N/A
核心差异一览
- Gemma 4 E2B is 1.0x cheaper for API usage (input + output tokens combined).
- DeepSeek V4 has a larger context window (1M vs 131K tokens).
- Gemma 4 E2B is faster at 260 tok/s vs 78 tok/s.
| Feature | DeepSeek V4 | Gemma 4 E2B |
|---|---|---|
Intelligence Index Composite reasoning & knowledge score (0-100) | 56 | 55 |
Provider | DeepSeek | |
Input Price Cost per million input tokens | $0.30 / 1M | $0.00 / 1M |
Output Price Cost per million output tokens | $1.20 / 1M | $0.00 / 1M |
Blended Price Weighted 7:2:1 cache:input:output per 1M | $0.20 / 1M | $0.00 / 1M |
Context Window Maximum input + output tokens | 1M tokens~1,500 A4 pages of text | 131.1K tokens~197 A4 pages of text |
Max Output Tokens Per single request | 66K | 8K |
Throughput Speed Tokens generated per second | 78 tok/s | 260 tok/s |
SWE-bench Verified Real-world software engineering | 62.8% | N/A |
GPQA Diamond Graduate-level reasoning | 78.4% | N/A |
Cached Input Price Prompt caching discount per 1M tokens | $0.03 / 1M | $0.00 / 1M |
各项指标谁更胜一筹？
At-a-glance decision frameworkEvaluations ongoing
权威基准测试得分对比
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.
Extremely difficult, closed-ended questions spanning the frontier of human expert knowledge.
Autonomous multi-step tasks executed inside a real command-line terminal environment.
Research-grade coding problems drawn from real scientific and numerical workloads.
Contamination-free competitive programming problems collected continuously over time.
American Invitational Mathematics Examination — multi-step Olympiad-level reasoning.
Accuracy when reasoning over very large inputs that fill the model context window.
Tokens emitted per second during active generation streaming.
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.
实时 Token 成本模拟器
Adjust sliders to calculate projected monthly cloud API spending with prompt caching discounts.
DeepSeek V4 · Strengths & Key Advantages
- 由 DeepSeek 研发的高精度 Next-Gen Sparse MoE with MLA 架构，支持 1M 上下文窗口
- 极具竞争力的 API 定价（输入 $0.3/1M，输出 $1.2/1M），支持高速推理与工具调用
- 在代码生成、逻辑推理与多语言处理基准测试中表现出色
Considerations & Trade-offs
- 超长上下文请求需要规划 Token 成本与并发配额
- 针对特定垂直领域任务建议结合系统提示词与检索增强（RAG）以获得最佳效果
Gemma 4 E2B · Strengths & Key Advantages
- 由 Google 研发的高精度 Ultra-compact edge transformer 架构，支持 131K 上下文窗口
- 极具竞争力的 API 定价（输入 $0/1M，输出 $0/1M），支持高速推理与工具调用
- 在代码生成、逻辑推理与多语言处理基准测试中表现出色
Considerations & Trade-offs
- 超长上下文请求需要规划 Token 成本与并发配额
- 针对特定垂直领域任务建议结合系统提示词与检索增强（RAG）以获得最佳效果
Token Use & Task Economics
Estimated cost and token consumption to complete one representative agentic task.
| Metric | DeepSeek V4 | Gemma 4 E2B |
|---|---|---|
Cost per Task Estimated USD to finish one task | $0.38 | $0.00 |
Output Tokens / Task Tokens generated per task | 48K | 2K |
Reasoning Tokens / Task Thinking tokens burned per task | 22K | 1K |
技术规格参数
Architecture, release timeline, capabilities, and modalities.
| Feature | DeepSeek V4 | Gemma 4 E2B |
|---|---|---|
| Official API Model ID (SDK) | deepseek-reasoner | models/gemma-4-e2b |
| API Protocol | OpenAI-Compatible REST / vLLM / SGLang / HuggingFace TGI | Google Gemini API (generateContent) + OpenAI-Compatible SDK |
| Release Date | 2026-07 | 2026-04 |
| Knowledge Cutoff | April 2026 | January 2026 |
| Architecture | Next-Gen Sparse MoE with MLA | Ultra-compact edge transformer |
| Parameters | MoE (Colossal) | E2B (2B) |
| 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) | Flat rate ($0.3/1M in) across full 1000K window | Free across full context window (only KV-cache VRAM scales) |
| Prompt Cache Policy & TTL | Automatic prefix caching at $0.03/1M (no extra cache-write fee) | Automatic prefix KV-caching in vLLM / SGLang (0 cost) |
| Computer Use / GUI Agent | API / CLI Tool-Use & DOM Agent ready (no raw pixel desktop click loop) | 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 | Supported via external Tavily/Exa Search & E2B/Docker Sandbox tools | Built-in Google Search Grounding + Managed Python Code Execution Sandbox |
| 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) | Text BPE (~4 chars/token) · Vision (~258–765 tokens per image tile) |
| Quantization & Min GPU VRAM | BF16 / FP16, FP8 Dynamic, INT4 AWQ / GPTQ, GGUF (Q4_K_M / Q8_0) · 43 GB VRAM (4-bit Quantized) · 151 GB VRAM (Native BF16) | BF16 / FP16, FP8 Dynamic, INT4 AWQ / GPTQ, GGUF (Q4_K_M / Q8_0) · 2 GB VRAM (4-bit Quantized) · 4 GB VRAM (Native BF16) |
| Safety & Frontier Classification | Open-Weights System Card + Customizable Guardrail Layer (Llama Guard / ShieldGemma) | Google DeepMind Frontier Safety Framework Verified |
| 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 | text, vision |
| Output Modalities | text | text |
DeepSeek V4 vs Gemma 4 E2B: 常见问题解答 (FAQ)
Which is more intelligent, DeepSeek V4 or Gemma 4 E2B?
DeepSeek V4 is more intelligent. It scores 56 compared with 55 on the composite Intelligence Index.
Which model is faster?
Gemma 4 E2B is faster, generating 260 tokens per second compared with 78 tokens per second.
Which model is cheaper to run?
Gemma 4 E2B is cheaper at $0.00 per 1M tokens blended, compared with $0.20 (7:2:1 cache:input:output ratio).
Which model has lower latency?
Gemma 4 E2B has lower latency, with a time to first token of 0.18s compared with 0.60s.
Which model has a larger context window?
DeepSeek V4 has a larger context window, supporting 1M tokens compared with 131.1K tokens.
综合结论
Verdict & Model Selection Recommendation: Choose DeepSeek V4 if your priority is Open-source enterprise deployments, large repository migrations, and long-horizon developer agents. Its performance in demanding tasks justifies its operational token cost. Choose Gemma 4 E2B if your workflow prioritizes Smartphones, IoT microcontrollers, and ultra-fast local keyword/entity extractors. For large-scale batch processing or customer-facing conversational interfaces, its balance of speed and economics delivers superior ROI.
常见问题 (FAQ)
How does DeepSeek V4 compare to Gemma 4 E2B in API token pricing and cost per token?+
DeepSeek V4 costs $0.30 per 1M input tokens and $1.20 per 1M output tokens, whereas Gemma 4 E2B costs $0.00 input and $0.00 output. Overall, Gemma 4 E2B 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: DeepSeek V4 or Gemma 4 E2B?+
DeepSeek V4 supports a context window of 1,000K tokens (~750k words), while Gemma 4 E2B supports 131K tokens (~98k words). Therefore, DeepSeek V4 can process more textual data and document context in a single prompt.
Which AI model is better for software engineering and coding: DeepSeek V4 or Gemma 4 E2B?+
DeepSeek V4 scores 62.8% on SWE-bench Verified, while Gemma 4 E2B scores competitively for software generation. For complex autonomous debugging and agentic engineering, DeepSeek V4 provides robust code generation and tool calling.
What is the difference in reasoning capability and extended thinking between DeepSeek V4 and Gemma 4 E2B?+
DeepSeek V4 features native chain-of-thought extended thinking mode with configurable reasoning effort. In contrast, Gemma 4 E2B provides chain-of-thought reasoning depth for complex math and science.
How does inference speed (tokens per second) compare between DeepSeek V4 and Gemma 4 E2B?+
DeepSeek V4 generates approximately 78 tokens per second, while Gemma 4 E2B delivers 260 tokens per second. In latency-sensitive production environments, Gemma 4 E2B provides faster time-to-first-token (TTFT) and higher streaming response velocity.
Can you implement a tiered LLM routing setup combining DeepSeek V4 and Gemma 4 E2B to optimize costs?+
Yes! High-efficiency architectures route high-frequency routine classification and conversational tasks to Gemma 4 E2B to save operational costs, and dynamically escalate complex architectural queries or reasoning tasks to DeepSeek V4. 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.
Related reading
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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.
数据来源: GetInfoUs Independent AI Benchmarks & Official API Specs