Yi-Large vs Gemini 4 Argon
Benchmarks, API Token Pricing, Context Window & Speed Comparison
Related topics (4)
Compare Yi-Large by 01.AI against Gemini 4 Argon by Google across real-world coding, reasoning, token economics, and latency.
Changez et Comparez les Modèles
Select any AI models from our verified benchmark database to generate live side-by-side matrices.
Live comparison: no in-depth editorial review has been published for this pair yet.
Yi-Large
Modèle de Génération ClassiqueYi-Large de 01.AI est un modèle d’IA conçu pour les assistants conversationnels, la synthèse documentaire et les usages professionnels, offrant une fenêtre de contexte de 33K, une vitesse de 75 tokens/s, un indice d’intelligence de 79/100 e
$2.50 / 1M
$2.50 / 1M
32.8K tokens
N/A
Gemini 4 Argon
IA Multimodale NativeGemini 4 Argon de Google est un modèle d’IA conçu pour la compréhension multimodale native (texte, vision) et l’analyse de documents complexes, offrant une fenêtre de contexte de 1M, une vitesse de 115 tokens/s, un indice d’intelligence de
$2.00 / 1M
$10.00 / 1M
1M tokens
81.9%
Différences Clés en un Coup d’Œil
- Yi-Large is 1.9x cheaper for API usage (input + output tokens combined).
- Gemini 4 Argon has a larger context window (1M vs 33K tokens).
- Gemini 4 Argon is faster at 115 tok/s vs 75 tok/s.
| Caractéristique | Yi-Large | Gemini 4 Argon |
|---|---|---|
Intelligence Index Composite reasoning & knowledge score (0-100) | 79 | 97 |
Provider | 01.AI | |
Input Price Cost per million input tokens | $2.50 / 1M | $2.00 / 1M |
Output Price Cost per million output tokens | $2.50 / 1M | $10.00 / 1M |
Blended Price Weighted 7:2:1 cache:input:output per 1M | $0.93 / 1M | $1.47 / 1M |
Context Window Maximum input + output tokens | 32.8K tokens~49 A4 pages of text | 1M tokens~1,500 A4 pages of text |
Max Output Tokens Per single request | 4K | 1000K |
Throughput Speed Tokens generated per second | 75 tok/s | 115 tok/s |
SWE-bench Verified Real-world software engineering | N/A | 81.9% |
GPQA Diamond Graduate-level reasoning | 52% | 81.5% |
Cached Input Price Prompt caching discount per 1M tokens | $0.25 / 1M | $0.10 / 1M |
Qui gagne où ?
At-a-glance decision frameworkEvaluations ongoing
Scores de Benchmarks Vérifiés
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.
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.
Yi-Large · Strengths & Key Advantages
- Fenêtre de contexte de 33K et capacité de sortie maximale de 4K tokens (GPQA Diamond 52% · MMLU-Pro 76.5%)
- Débit rapide de 75 tokens/seconde avec une latence au premier token (TTFT) de 510 ms
- Tarification API optimisée ($2.5/$2.5 par million de tokens) avec prise en charge du cache de prompts et des schémas JSON stricts
Considerations & Trade-offs
- Modèle cloud propriétaire ne permettant pas le téléchargement local des poids sur serveur isolé
- Pour les charges à très fort volume, l’activation du cache de prompts et de l’API Batch est recommandée
Gemini 4 Argon · Strengths & Key Advantages
- Fenêtre de contexte de 1M et capacité de sortie maximale de 1M tokens (SWE-bench Verified 81.9% · GPQA Diamond 81.5%)
- Débit rapide de 115 tokens/seconde avec une latence au premier token (TTFT) de 850 ms
- Tarification API optimisée ($2/$10 par million de tokens) avec prise en charge du cache de prompts et des schémas JSON stricts
Considerations & Trade-offs
- Modèle cloud propriétaire ne permettant pas le téléchargement local des poids sur serveur isolé
- Pour les charges à très fort volume, l’activation du cache de prompts et de l’API Batch est recommandée
Token Use & Task Economics
Estimated cost and token consumption to complete one representative agentic task.
| Metric | Yi-Large | Gemini 4 Argon |
|---|---|---|
Cost per Task Estimated USD to finish one task | $0.15 | $2.45 |
Output Tokens / Task Tokens generated per task | 14K | 75K |
Reasoning Tokens / Task Thinking tokens burned per task | 0K | 36K |
Spécifications Techniques
Architecture, release timeline, capabilities, and modalities.
| Caractéristique | Yi-Large | Gemini 4 Argon |
|---|---|---|
| Official API Model ID (SDK) | yi-large | models/gemini-4-argon |
| API Protocol | 01.AI REST API (OpenAI-Compatible) | Google Gemini API (generateContent) + OpenAI-Compatible SDK |
| Release Date | 2024-05 | 2026-09 |
| Knowledge Cutoff | March 2024 | June 2026 |
| Architecture | Dense Bilingual Foundation Transformer | Pathways Multimodal Sparse Mixture-of-Experts |
| Parameters | Confidential | Confidential |
| Enterprise Rate Limits (RPM / TPM) | 5,000 – 15,000 RPM (Production Tier) · 800,000 – 2,000,000 TPM | 10,000 – 30,000 RPM (Tier 5 Enterprise) · 4,000,000 – 10,000,000 TPM |
| Long-Context Tier (>128K/200K) | Flat rate ($2.5/1M in) across full 33K window | Standard rate up to 200K tokens; 2x input/output tier above 200K tokens |
| Prompt Cache Policy & TTL | Cached input at $0.25/1M tokens | Implicit + Explicit Context Caching at $0.1/1M with configurable TTL |
| 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 | API Prompt Engineering & RAG Distillation | Vertex AI Supervised Adapter Tuning & Context Distillation |
| Multimodal Token Billing | Text BPE (~4 chars/token) | 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 | 01.AI Production Safety & Alignment 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 | text, vision, audio, video |
| Output Modalities | text | text |
Yi-Large vs Gemini 4 Argon: Réponses Rapides (FAQ)
Which is more intelligent, Yi-Large or Gemini 4 Argon?
Gemini 4 Argon is more intelligent. It scores 97 compared with 79 on the composite Intelligence Index.
Which model is faster?
Gemini 4 Argon is faster, generating 115 tokens per second compared with 75 tokens per second.
Which model is cheaper to run?
Yi-Large is cheaper at $0.93 per 1M tokens blended, compared with $1.47 (7:2:1 cache:input:output ratio).
Which model has lower latency?
Yi-Large has lower latency, with a time to first token of 0.51s compared with 0.85s.
Which model has a larger context window?
Gemini 4 Argon has a larger context window, supporting 1M tokens compared with 32.8K tokens.
Verdict
Choose Yi-Large for conçu pour les assistants conversationnels, la synthèse documentaire et les usages professionnels, l’intégration rag et les pipelines de production évolutifs., or choose Gemini 4 Argon for conçu pour la compréhension multimodale native (texte, vision) et l’analyse de documents complexes, l’intégration rag et les pipelines de production évolutifs..
Questions fréquentes
Which is cheaper: Yi-Large or Gemini 4 Argon?+
Yi-Large costs $2.5/1M input and $2.5/1M output tokens, while Gemini 4 Argon costs $2/1M input and $10/1M output tokens.
Which model has a larger context window: Yi-Large or Gemini 4 Argon?+
Yi-Large supports 33K 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.
Related reading
- Aion-3.0 vs Aion-Mini : Comparatif Benchmarks, Tarifs API et Vitesse (2026)Comparer
- Aion-3.0 vs Gemini 3.6 Flash : Comparatif Benchmarks, Tarifs API et Vitesse (2026)Comparer
- Aion-Mini vs Laguna XS 2.1 : Comparatif Benchmarks, Tarifs API et Vitesse (2026)Comparer
- Aya Expanse 32B vs Llama 3.2 90B Vision : Comparatif Benchmarks, Tarifs API et Vitesse (2026)Comparer
- Aya Expanse 8B vs Command R+ : Comparatif Benchmarks, Tarifs API et Vitesse (2026)Comparer
- Aya Expanse 8B vs Qwen 2.5 7B Instruct : Comparatif Benchmarks, Tarifs API et Vitesse (2026)Comparer
Les scores de benchmarks, tarifs par token et limites évoluent rapidement. Vérifiez toujours les spécifications actuelles sur la documentation officielle du fournisseur.
Sources: 01.AI & Google Official Documentation