Gemma 4 31B vs Gemma 4 26B A4B
Benchmarks, API Token Pricing, Context Window & Speed Comparison
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Gemma 4 31B by Google and Gemma 4 26B A4B 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, both models offer identical input token rates. On output generation, costs are matched. In data-heavy architectures requiring extensive repository or document analysis, both models provide an identical memory horizon with 262K tokens. In latency-sensitive production environments, Gemma 4 26B A4B 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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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%
Gemma 4 26B A4B
MoE eficienteHighly efficient abierto MoE modelo activating solo 4B parámetros por token para blazing rápido local inferencia.
$0.00 / 1M
$0.00 / 1M
262.1K tokens
61%
Diferencias Clave de un Vistazo
- Both models cost the same under this workload — choose on capability, not price.
- Gemma 4 26B A4B is faster at 160 tok/s vs 80 tok/s.
- Gemma 4 31B leads on SWE-bench coding (68.5% vs 61.0%).
| Característica | Gemma 4 31B | Gemma 4 26B A4B |
|---|---|---|
Intelligence Index Composite reasoning & knowledge score (0-100) | 81 | 74 |
Provider | ||
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 | 262.1K tokens~393 A4 pages of text |
Max Output Tokens Per single request | 33K | 33K |
Throughput Speed Tokens generated per second | 80 tok/s | 160 tok/s |
SWE-bench Verified Real-world software engineering | 68.5% | 61% |
GPQA Diamond Graduate-level reasoning | 77% | 71.5% |
¿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.
American Invitational Mathematics Examination — multi-step Olympiad-level reasoning.
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
Gemma 4 26B A4B · Strengths & Key Advantages
- 4B activo inferencia compute con 26B parámetro expressiveness
- alto rendimiento en consumidor workstations
- 262K ventana de contexto
Considerations & Trade-offs
- requiere enough RAM un hold todos 26B weights
- Slightly menos robust en perimetral-case lógica que denso 31B
Token Use & Task Economics
Estimated cost and token consumption to complete one representative agentic task.
| Metric | Gemma 4 31B | Gemma 4 26B A4B |
|---|---|---|
Cost per Task Estimated USD to finish one task | $0.00 | $0.00 |
Output Tokens / Task Tokens generated per task | 12K | 8K |
Reasoning Tokens / Task Thinking tokens burned per task | 6K | 3K |
Especificaciones Técnicas
Architecture, release timeline, capabilities, and modalities.
| Característica | Gemma 4 31B | Gemma 4 26B A4B |
|---|---|---|
| Official API Model ID (SDK) | models/gemma-4-31b | models/gemma-4-26b-a4b |
| API Protocol | Google Gemini API (generateContent) + OpenAI-Compatible SDK | Google Gemini API (generateContent) + OpenAI-Compatible SDK |
| Release Date | 2026-04 | 2026-04 |
| Knowledge Cutoff | January 2026 | January 2026 |
| Architecture | Dense open reasoning transformer with native thinking | Sparse Mixture-of-Experts open transformer (26B total, 4B active) |
| Parameters | 31B | 26B (4B active MoE) |
| 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 | 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) · Vision (~258–765 tokens per image tile) | 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) · 19 GB VRAM (4-bit Quantized) · 67 GB VRAM (Native BF16) | BF16 / FP16, FP8 Dynamic, INT4 AWQ / GPTQ, GGUF (Q4_K_M / Q8_0) · 16 GB VRAM (4-bit Quantized) · 56 GB VRAM (Native BF16) |
| Safety & Frontier Classification | Google DeepMind Frontier Safety Framework Verified | 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, vision | text, vision |
| Output Modalities | text | text |
Gemma 4 31B vs Gemma 4 26B A4B: Respuestas Rápidas (FAQ)
Which is more intelligent, Gemma 4 31B or Gemma 4 26B A4B?
Gemma 4 31B is more intelligent. It scores 81 compared with 74 on the composite Intelligence Index.
Which model is faster?
Gemma 4 26B A4B is faster, generating 160 tokens per second compared with 80 tokens per second.
Which model has lower latency?
Gemma 4 26B A4B has lower latency, with a time to first token of 0.29s compared with 0.48s.
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 Gemma 4 26B A4B if your workflow prioritizes High-throughput local serving, private chatbots, and fast document analysis. 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 Gemma 4 26B A4B in API token pricing and cost per token?+
Both Gemma 4 31B and Gemma 4 26B A4B 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.
Which model has a larger context window: Gemma 4 31B or Gemma 4 26B A4B?+
Both Gemma 4 31B and Gemma 4 26B A4B offer an identical context window of 262K tokens (~197k words). Both architectures provide equal capacity for large document ingestion, multi-file codebases, and extended multi-turn conversation histories.
Which AI model is better for software engineering and coding: Gemma 4 31B or Gemma 4 26B A4B?+
Gemma 4 31B scores 68.5% on SWE-bench Verified while Gemma 4 26B A4B scores 61%. 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 Gemma 4 26B A4B?+
Gemma 4 31B features native chain-of-thought extended thinking mode with configurable reasoning effort. In contrast, Gemma 4 26B A4B provides chain-of-thought reasoning depth for complex math and science.
How does inference speed (tokens per second) compare between Gemma 4 31B and Gemma 4 26B A4B?+
Gemma 4 31B generates approximately 80 tokens per second, while Gemma 4 26B A4B delivers 160 tokens per second. In latency-sensitive production environments, Gemma 4 26B A4B 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 Gemma 4 26B A4B 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 Gemma 4 26B A4B, 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.
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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