Gemma 4 31B vs Llama 3.3 70B
Benchmarks, API Token Pricing, Context Window & Speed Comparison
Related topics (12)
Gemma 4 31B by Google and Llama 3.3 70B by Meta 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, both models offer identical input token rates. On output generation, costs are matched. In data-heavy architectures requiring extensive repository or document analysis, Gemma 4 31B provides an expanded memory horizon with 262K tokens. In latency-sensitive production environments, Llama 3.3 70B 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.
Cambia y Compara Cualquier Modelo
Select any AI models from our verified benchmark database to generate live side-by-side matrices.
Gemma 4 31B
Razonamiento abiertoLargest denso abierto modelo de razonamiento en el Gemma 4 series con 262K contexto y nativo cadena de pensamiento.
$0.00 / 1M
$0.00 / 1M
262.1K tokens
68.5%
Llama 3.3 70B
Titán de pesos abiertosEl modelo fundacional de pesos abiertos insignia de Meta que ofrece un rendimiento un la par de los modelos anteriores de 405B con mucho menor costo computacional.
$0.00 / 1M
$0.00 / 1M
128K tokens
39.5%
Diferencias Clave de un Vistazo
- 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%).
| Característica | Gemma 4 31B | Llama 3.3 70B |
|---|---|---|
Intelligence Index Composite reasoning & knowledge score (0-100) | 81 | 38 |
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 | $0.07 / 1M |
Context Window Maximum input + output tokens | 262.1K tokens~393 A4 pages of text | 128K tokens~192 A4 pages of text |
Max Output Tokens Per single request | 33K | 4K |
Throughput Speed Tokens generated per second | 80 tok/s | 280 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 |
¿Quién gana en cada área?
At-a-glance decision frameworkPuntuaciones de Benchmarks Verificadas
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.
Latencia y Tiempo de Respuesta
Time to first token (TTFT) and estimated end-to-end response time for a typical 500-token response.
Simulador Interactivo de Costos de Tokens
Adjust sliders to calculate projected monthly cloud API spending with prompt caching discounts.
Gemma 4 31B · Strengths & Key Advantages
- de frontera-grade razonamiento en un abierto-weight 31B package
- Expansive 262K ventana de contexto
- competitivo codificación y matemáticas benchmark scores
Considerations & Trade-offs
- requiere 32GB+ VRAM o 4-bit quantisation un run locally
- Slower generación velocidad cuando cadena de pensamiento es activo
Llama 3.3 70B · Strengths & Key Advantages
- 100% de pesos abiertos allowing completo datos soberanía, privado hosting, y ajuste fino
- Lightning-rápido rendimiento (up un 280 tok/s en especializado hardware inferencia providers)
- Economical cloud API precios ($0.20 entrada / $0.70 salida por millones tokens)
- Industry estándar para local offline AI y empresarial cumplimiento
Considerations & Trade-offs
- Knowledge cutoff en late 2023 requiere RAG para recent events
- Smaller maximum salida token generación limit (4K tokens)
Token Use & Task Economics
Estimated cost and token consumption to complete one representative agentic task.
| Metric | Gemma 4 31B | Llama 3.3 70B |
|---|---|---|
Cost per Task Estimated USD to finish one task | $0.00 | $0.21 |
Output Tokens / Task Tokens generated per task | 12K | 45K |
Reasoning Tokens / Task Thinking tokens burned per task | 6K | 0K |
Especificaciones Técnicas
Architecture, release timeline, capabilities, and modalities.
| Característica | Gemma 4 31B | Llama 3.3 70B |
|---|---|---|
| Official API Model ID (SDK) | models/gemma-4-31b | meta-llama/Llama-3.3-70B |
| API Protocol | Google Gemini API (generateContent) + OpenAI-Compatible SDK | OpenAI-Compatible REST / vLLM / SGLang / HuggingFace TGI |
| Release Date | 2026-04 | 2024-12 |
| Knowledge Cutoff | January 2026 | December 2023 |
| Architecture | Dense open reasoning transformer with native thinking | Dense Autoregressive Transformer |
| Parameters | 31B | 70B |
| 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) | API / CLI Tool-Use & DOM Agent ready (no raw pixel desktop click loop) |
| 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) |
| 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) · 43 GB VRAM (4-bit Quantized) · 151 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 |
| Output Modalities | text | text |
Gemma 4 31B vs Llama 3.3 70B: Respuestas Rápidas (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.
Veredicto
Verdict & Model Selection Recommendation: Choose Gemma 4 31B if your priority is Private enterprise reasoning servers, local software development, and deep research. Its performance in demanding tasks justifies its operational token cost. Choose Llama 3.3 70B if your workflow prioritizes Private on-premises deployments, high-throughput text summarization, and fine-tuned domain tasks. For large-scale batch processing or customer-facing conversational interfaces, its balance of speed and economics delivers superior ROI.
Preguntas frecuentes
How does Gemma 4 31B compare to Llama 3.3 70B in API token pricing and cost per token?+
Both Gemma 4 31B and Llama 3.3 70B are open-weights foundation models available at $0.00 direct API cost when self-hosted, or offered at identical baseline rates across major inference providers. Model selection depends on GPU deployment sizing and specialized benchmark performance rather than raw token price. Both models support prompt caching discounts of up to 90% for repeated prompt prefixes.
Which model has a larger context window: Gemma 4 31B or Llama 3.3 70B?+
Gemma 4 31B supports a context window of 262K tokens (~197k words), while Llama 3.3 70B supports 128K tokens (~96k words). Therefore, Gemma 4 31B can process more textual data and document context in a single prompt.
Which AI model is better for software engineering and coding: Gemma 4 31B or Llama 3.3 70B?+
Gemma 4 31B scores 68.5% on SWE-bench Verified while Llama 3.3 70B scores 39.5%. For autonomous software engineering and multi-file debugging, Gemma 4 31B holds the verified benchmark lead.
What is the difference in reasoning capability and extended thinking between Gemma 4 31B and Llama 3.3 70B?+
Gemma 4 31B features native chain-of-thought extended thinking mode with configurable reasoning effort. In contrast, Llama 3.3 70B focuses on standard fast inference.
How does inference speed (tokens per second) compare between Gemma 4 31B and Llama 3.3 70B?+
Gemma 4 31B generates approximately 80 tokens per second, while Llama 3.3 70B delivers 280 tokens per second. In latency-sensitive production environments, Llama 3.3 70B provides faster time-to-first-token (TTFT) and higher streaming response velocity.
Can you implement a tiered LLM routing setup combining Gemma 4 31B and Llama 3.3 70B to optimize costs?+
Yes! Tiered multi-model routing can optimize for latency and accuracy: you can route high-frequency conversational turns and latency-sensitive queries to Llama 3.3 70B, and route deep architectural or complex reasoning queries to Gemma 4 31B. This balanced routing strategy maximizes system throughput and answer quality.
Understand key metrics like context window, token pricing, reasoning mode, and SWE-bench scores in our comprehensive AI glossary.
Related reading
- Command R7B vs Llama 3.3 70B: Benchmarks, Pricing & Speed ComparisonComparar
- DeepSeek V3 vs Llama 3.3 70B: Benchmarks, Pricing & Speed ComparisonComparar
- Gemma 4 31B vs Gemma 4 26B A4B: Benchmarks, Pricing & Speed ComparisonComparar
- Gemma 4 31B vs Mistral Small 4: Benchmarks, Pricing & Speed ComparisonComparar
- Gemma 4 E2B vs Gemma 4 31B: Benchmarks, Pricing & Speed ComparisonComparar
- Llama 3.3 70B vs GPT-4o mini: Benchmarks, Pricing & Speed ComparisonComparar
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: GetInfoUs Independent AI Benchmarks & Official API Specs