Gemma 3 vs Gemma 2
Benchmarks, API Token Pricing, Context Window & Speed Comparison
Related topics (12)
Gemma 3 by Google and Gemma 2 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, Gemma 3 provides an expanded memory horizon with 131K tokens. In latency-sensitive production environments, Gemma 2 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.
Changez et Comparez les Modèles
Select any AI models from our verified benchmark database to generate live side-by-side matrices.
Gemma 3
Multimodal open sourcemultimodal ouvert modèle family avec 128K fenêtre de contexte, natif vision compréhension, et multi-lingual support.
$0.00 / 1M
$0.00 / 1M
131.1K tokens
N/A
Gemma 2
Open source efficaceSubstantially upgraded à poids ouverts family intégrant industry-leading performances par paramètre.
$0.00 / 1M
$0.00 / 1M
8.2K tokens
N/A
Différences Clés en un Coup d’Œil
- Both models cost the same under this workload — choose on capability, not price.
- Gemma 3 has a larger context window (131K vs 8K tokens).
- Gemma 2 is faster at 110 tok/s vs 105 tok/s.
| Caractéristique | Gemma 3 | Gemma 2 |
|---|---|---|
Intelligence Index Composite reasoning & knowledge score (0-100) | 68 | 58 |
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 | 131.1K tokens~197 A4 pages of text | 8.2K tokens~12 A4 pages of text |
Max Output Tokens Per single request | 16K | 4K |
Throughput Speed Tokens generated per second | 105 tok/s | 110 tok/s |
SWE-bench Verified Real-world software engineering | N/A | N/A |
GPQA Diamond Graduate-level reasoning | 61% | N/A |
Qui gagne où ?
At-a-glance decision frameworkEvaluations ongoing
Scores de Benchmarks Vérifiés
Official evaluation metrics verified by independent third-party research labs.
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.
Tokens emitted per second during active generation streaming.
Latence et Temps de Réponse
Time to first token (TTFT) and estimated end-to-end response time for a typical 500-token response.
Simulateur Interactif de Coût de Tokens
Adjust sliders to calculate projected monthly cloud API spending with prompt caching discounts.
Gemma 3 · Strengths & Key Advantages
- natif vision ingestion dans un ouvert weight modèle
- Spacious 128K fenêtre de contexte
- solide performances à travers 100+ langues
Considerations & Trade-offs
- nécessite 24GB+ VRAM pour le 27B variant
- manque d'étendu chaîne de pensée mode
Gemma 2 · Strengths & Key Advantages
- classe-leading 9B et 27B paramètre efficacité
- faible mémoire empreinte sur Mac et RTX GPUs
- Excellent suivi des instructions
Considerations & Trade-offs
- fenêtre de contexte remains capped à 8K
- No natif image ou audio support
Token Use & Task Economics
Estimated cost and token consumption to complete one representative agentic task.
| Metric | Gemma 3 | Gemma 2 |
|---|---|---|
Cost per Task Estimated USD to finish one task | $0.00 | $0.00 |
Output Tokens / Task Tokens generated per task | 5K | 3K |
Reasoning Tokens / Task Thinking tokens burned per task | 0K | 0K |
Spécifications Techniques
Architecture, release timeline, capabilities, and modalities.
| Caractéristique | Gemma 3 | Gemma 2 |
|---|---|---|
| Official API Model ID (SDK) | models/gemma-3 | models/gemma-2 |
| API Protocol | Google Gemini API (generateContent) + OpenAI-Compatible SDK | Google Gemini API (generateContent) + OpenAI-Compatible SDK |
| Release Date | 2025-03 | 2024-06 |
| Knowledge Cutoff | December 2024 | May 2024 |
| Architecture | Native multimodal open transformer with expanded context | Dense transformer with sliding window attention and soft-capping |
| Parameters | 1B / 4B / 12B / 27B | 2B / 9B / 27B |
| 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 | 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) |
| Quantization & Min GPU VRAM | BF16 / FP16, FP8 Dynamic, INT4 AWQ / GPTQ, GGUF (Q4_K_M / Q8_0) · 2 GB VRAM (4-bit Quantized) · 4 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 | 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 |
| Output Modalities | text | text |
Gemma 3 vs Gemma 2: Réponses Rapides (FAQ)
Which is more intelligent, Gemma 3 or Gemma 2?
Gemma 3 is more intelligent. It scores 68 compared with 58 on the composite Intelligence Index.
Which model is faster?
Gemma 2 is faster, generating 110 tokens per second compared with 105 tokens per second.
Which model has lower latency?
Gemma 2 has lower latency, with a time to first token of 0.30s compared with 0.32s.
Which model has a larger context window?
Gemma 3 has a larger context window, supporting 131.1K tokens compared with 8.2K tokens.
Verdict
Verdict & Model Selection Recommendation: Choose Gemma 3 if your priority is Self-hosted multimodal applications, local image QA, and long document parsing. Its performance in demanding tasks justifies its operational token cost. Choose Gemma 2 if your workflow prioritizes Local developer assistants, private enterprise fine-tuning, and offline inference. For large-scale batch processing or customer-facing conversational interfaces, its balance of speed and economics delivers superior ROI.
Questions fréquentes
How does Gemma 3 compare to Gemma 2 in API token pricing and cost per token?+
Both Gemma 3 and Gemma 2 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 3 or Gemma 2?+
Gemma 3 supports a context window of 131K tokens (~98k words), while Gemma 2 supports 8K tokens (~6k words). Therefore, Gemma 3 can process more textual data and document context in a single prompt.
Which AI model is better for software engineering and coding: Gemma 3 or Gemma 2?+
Gemma 3 scores at the frontier of coding benchmarks, while Gemma 2 scores competitively for software generation. For complex autonomous debugging and agentic engineering, Gemma 3 provides robust code generation and tool calling.
What is the difference in reasoning capability and extended thinking between Gemma 3 and Gemma 2?+
Gemma 3 is optimized for low-latency direct generation. In contrast, Gemma 2 focuses on standard fast inference.
How does inference speed (tokens per second) compare between Gemma 3 and Gemma 2?+
Gemma 3 generates approximately 105 tokens per second, while Gemma 2 delivers 110 tokens per second. In latency-sensitive production environments, Gemma 2 provides faster time-to-first-token (TTFT) and higher streaming response velocity.
Can you implement a tiered LLM routing setup combining Gemma 3 and Gemma 2 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 2, and route deep architectural or complex reasoning queries to Gemma 3. 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
- ERNIE Lite vs Gemma 2: Benchmarks, Pricing & Speed ComparisonComparer
- Gemma 3 vs Llama 4 Scout: Benchmarks, Pricing & Speed ComparisonComparer
- Gemma 3n vs Gemma 3: Benchmarks, Pricing & Speed ComparisonComparer
- Aion-3.0 vs Aion-Mini: Benchmarks, Pricing & Speed ComparisonComparer
- Aion-3.0 vs Gemini 3.6 Flash: Benchmarks, Pricing & Speed ComparisonComparer
- Aion-Mini vs Laguna XS 2.1: Benchmarks, Pricing & Speed ComparisonComparer
Les scores de benchmarks, tarifs par token et limites évoluent rapidement. Vérifiez toujours les spécifications actuelles sur la documentation officielle du fournisseur.
Sources: GetInfoUs Independent AI Benchmarks & Official API Specs