Gemini 2.5 Pro vs Gemini 4 Argon
Benchmarks, API Token Pricing, Context Window & Speed Comparison
Related topics (4)
Compare Gemini 2.5 Pro by Google against Gemini 4 Argon by Google across real-world coding, reasoning, token economics, and latency.
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Gemini 2.5 Pro
Deep-Reasoning-EngineGemini 2.5 Pro von Google ist ein KI-Modell für tiefgehendes logisches Denken, komplexe Mathematik und wissenschaftliche Analysen mit 1.0M Kontextfenster, 95 Token/s Durchsatz, einem Intelligenz-Index von 78/100 und $1.25/$10 pro 1M Token.
$1.25 / 1M
$10.00 / 1M
1.05M tokens
63.8%
Gemini 4 Argon
Nativ Multimodale KIGemini 4 Argon von Google ist ein KI-Modell für nativ multimodale Verarbeitung von Text und Bild sowie visuelle Dokumentenanalyse mit 1M Kontextfenster, 115 Token/s Durchsatz, einem Intelligenz-Index von 97/100 und $2/$10 pro 1M Token.
$2.00 / 1M
$10.00 / 1M
1M tokens
81.9%
Wichtigste Unterschiede auf einen Blick
- Gemini 2.5 Pro is 1.1x cheaper for API usage (input + output tokens combined).
- Gemini 2.5 Pro has a larger context window (1M vs 1M tokens).
- Gemini 4 Argon is faster at 115 tok/s vs 95 tok/s.
- Gemini 4 Argon leads on SWE-bench coding (81.9% vs 63.8%).
| Feature | Gemini 2.5 Pro | Gemini 4 Argon |
|---|---|---|
Intelligence Index Composite reasoning & knowledge score (0-100) | 78 | 97 |
Provider | ||
Input Price Cost per million input tokens | $1.25 / 1M | $2.00 / 1M |
Output Price Cost per million output tokens | $10.00 / 1M | $10.00 / 1M |
Blended Price Weighted 7:2:1 cache:input:output per 1M | $1.47 / 1M | $1.47 / 1M |
Context Window Maximum input + output tokens | 1.05M tokens~1,573 A4 pages of text | 1M tokens~1,500 A4 pages of text |
Max Output Tokens Per single request | 66K | 1000K |
Throughput Speed Tokens generated per second | 95 tok/s | 115 tok/s |
SWE-bench Verified Real-world software engineering | 63.8% | 81.9% |
GPQA Diamond Graduate-level reasoning | 84% | 81.5% |
Cached Input Price Prompt caching discount per 1M tokens | $0.31 / 1M | $0.10 / 1M |
Wer gewinnt in welcher Disziplin?
At-a-glance decision frameworkGeprüfte Benchmark-Ergebnisse im Vergleich
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.
Latenz & Antwortzeit
Time to first token (TTFT) and estimated end-to-end response time for a typical 500-token response.
Interaktiver Token-Kosten-Simulator
Adjust sliders to calculate projected monthly cloud API spending with prompt caching discounts.
Gemini 2.5 Pro · Strengths & Key Advantages
- 1.0M Kontextfenster und bis zu 66K maximale Output-Token (SWE-bench Verified 63.8% · GPQA Diamond 84%)
- Hohe Generierungsgeschwindigkeit von 95 Token/Sekunde bei nur 820 ms Erstlatenz (TTFT)
- Effiziente API-Struktur ($1.25/$10 pro 1M Token) inklusive Prompt-Caching, Tool-Calling und strikter JSON-Ausgabe
Considerations & Trade-offs
- Proprietäres Cloud-Modell ohne Möglichkeit zum lokalen Download der Modellgewichte
- Bei extrem komplexen mehrstufigen Forschungsfragen bieten schwere Reasoning-Spezialmodelle noch tiefere Analysen
Gemini 4 Argon · Strengths & Key Advantages
- 1M Kontextfenster und bis zu 1M maximale Output-Token (SWE-bench Verified 81.9% · GPQA Diamond 81.5%)
- Hohe Generierungsgeschwindigkeit von 115 Token/Sekunde bei nur 850 ms Erstlatenz (TTFT)
- Effiziente API-Struktur ($2/$10 pro 1M Token) inklusive Prompt-Caching, Tool-Calling und strikter JSON-Ausgabe
Considerations & Trade-offs
- Proprietäres Cloud-Modell ohne Möglichkeit zum lokalen Download der Modellgewichte
- Bei hohem Anfragevolumen empfiehlt sich der Einsatz von Prompt-Caching und Batch-API zur Kostenoptimierung
Token Use & Task Economics
Estimated cost and token consumption to complete one representative agentic task.
| Metric | Gemini 2.5 Pro | Gemini 4 Argon |
|---|---|---|
Cost per Task Estimated USD to finish one task | $1.45 | $2.45 |
Output Tokens / Task Tokens generated per task | 45K | 75K |
Reasoning Tokens / Task Thinking tokens burned per task | 22K | 36K |
Technische Spezifikationen
Architecture, release timeline, capabilities, and modalities.
| Feature | Gemini 2.5 Pro | Gemini 4 Argon |
|---|---|---|
| Official API Model ID (SDK) | models/gemini-2-5-pro | models/gemini-4-argon |
| API Protocol | Google Gemini API (generateContent) + OpenAI-Compatible SDK | Google Gemini API (generateContent) + OpenAI-Compatible SDK |
| Release Date | 2025-03 | 2026-09 |
| Knowledge Cutoff | January 2025 | June 2026 |
| Architecture | Pathways Multimodal Sparse Mixture-of-Experts with Thinking | Pathways Multimodal Sparse Mixture-of-Experts |
| Parameters | Confidential | Confidential |
| Enterprise Rate Limits (RPM / TPM) | 10,000 – 30,000 RPM (Tier 5 Enterprise) · 4,000,000 – 10,000,000 TPM | 10,000 – 30,000 RPM (Tier 5 Enterprise) · 4,000,000 – 10,000,000 TPM |
| Long-Context Tier (>128K/200K) | Standard rate up to 200K tokens; 2x input/output tier above 200K tokens | Standard rate up to 200K tokens; 2x input/output tier above 200K tokens |
| Prompt Cache Policy & TTL | Implicit + Explicit Context Caching at $0.31/1M with configurable TTL | Implicit + Explicit Context Caching at $0.1/1M with configurable TTL |
| 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 | Vertex AI Supervised Adapter Tuning & Context Distillation | Vertex AI Supervised Adapter Tuning & Context Distillation |
| Multimodal Token Billing | Text BPE (~4 chars/token) · Vision (~258–765 tokens per image tile) · Audio (~25–32 tokens/sec) · Video (~258–300 tokens/sec) | Text BPE (~4 chars/token) · Vision (~258–765 tokens per image tile) · Audio (~25–32 tokens/sec) · Video (~258–300 tokens/sec) |
| Quantization & Min GPU VRAM | Cloud-Hosted Proprietary (FP8 / BF16 Tensor Core Cluster) · Zero Local VRAM Required (100% Serverless Cloud API) | Cloud-Hosted Proprietary (FP8 / BF16 Tensor Core Cluster) · Zero Local VRAM Required (100% Serverless Cloud API) |
| Safety & Frontier Classification | Google DeepMind Frontier Safety Framework Verified | Google DeepMind Frontier Safety Framework Verified |
| Enterprise Compliance (ZDR/HIPAA/SOC2) | SOC2 Type II · GDPR · Zero Data Retention (ZDR) & HIPAA BAA Available on Enterprise Tier | SOC2 Type II · GDPR · Zero Data Retention (ZDR) & HIPAA BAA Available on Enterprise Tier |
| Input Modalities | text, vision, audio, video | text, vision, audio, video |
| Output Modalities | text | text |
Gemini 2.5 Pro vs Gemini 4 Argon: Schnelle Antworten (FAQ)
Which is more intelligent, Gemini 2.5 Pro or Gemini 4 Argon?
Gemini 4 Argon is more intelligent. It scores 97 compared with 78 on the composite Intelligence Index.
Which model is faster?
Gemini 4 Argon is faster, generating 115 tokens per second compared with 95 tokens per second.
Which model has lower latency?
Gemini 2.5 Pro has lower latency, with a time to first token of 0.82s compared with 0.85s.
Which model has a larger context window?
Gemini 2.5 Pro has a larger context window, supporting 1.05M tokens compared with 1M tokens.
Fazit
Choose Gemini 2.5 Pro for optimal für tiefgehendes logisches denken, komplexe mathematik und wissenschaftliche analysen, enterprise-rag-systeme und skalierbare api-pipelines., or choose Gemini 4 Argon for optimal für nativ multimodale verarbeitung von text und bild sowie visuelle dokumentenanalyse, enterprise-rag-systeme und skalierbare api-pipelines..
Häufig gestellte Fragen (FAQ)
Which is cheaper: Gemini 2.5 Pro or Gemini 4 Argon?+
Gemini 2.5 Pro costs $1.25/1M input and $10/1M output tokens, while Gemini 4 Argon costs $2/1M input and $10/1M output tokens.
Which model has a larger context window: Gemini 2.5 Pro or Gemini 4 Argon?+
Gemini 2.5 Pro supports 1.0M tokens whereas Gemini 4 Argon supports 1M tokens.
Understand key metrics like context window, token pricing, reasoning mode, and SWE-bench scores in our comprehensive AI glossary.
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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.
Offizielle Quellen: Google & Google Official Documentation