Gemini 4 Argon vs Qwen3.8 Max Preview
Benchmarks, API Token Pricing, Context Window & Speed Comparison
Related topics (4)
Compare Gemini 4 Argon by Google against Qwen3.8 Max Preview by Alibaba 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.
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%
Qwen3.8 Max Preview
Modèle de Base à Poids OuvertsQwen3.8 Max Preview de Alibaba est un modèle d’IA conçu pour l’auto-hébergement sur infrastructure privée, le fine-tuning et la souveraineté des données, offrant une fenêtre de contexte de 1M, une vitesse de 60 tokens/s, un indice d’intelli
$0.00 / 1M
$0.00 / 1M
1M tokens
75.2%
Différences Clés en un Coup d’Œil
- Qwen3.8 Max Preview is 1.0x cheaper for API usage (input + output tokens combined).
- Gemini 4 Argon is faster at 115 tok/s vs 60 tok/s.
- Gemini 4 Argon leads on SWE-bench coding (81.9% vs 75.2%).
| Caractéristique | Gemini 4 Argon | Qwen3.8 Max Preview |
|---|---|---|
Intelligence Index Composite reasoning & knowledge score (0-100) | 97 | 93 |
Provider | Alibaba | |
Input Price Cost per million input tokens | $2.00 / 1M | $0.00 / 1M |
Output Price Cost per million output tokens | $10.00 / 1M | $0.00 / 1M |
Blended Price Weighted 7:2:1 cache:input:output per 1M | $1.47 / 1M | $0.00 / 1M |
Context Window Maximum input + output tokens | 1M tokens~1,500 A4 pages of text | 1M tokens~1,500 A4 pages of text |
Max Output Tokens Per single request | 1000K | 32K |
Throughput Speed Tokens generated per second | 115 tok/s | 60 tok/s |
SWE-bench Verified Real-world software engineering | 81.9% | 75.2% |
GPQA Diamond Graduate-level reasoning | 81.5% | 79.5% |
Cached Input Price Prompt caching discount per 1M tokens | $0.10 / 1M | $0.00 / 1M |
Qui gagne où ?
At-a-glance decision frameworkScores 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.
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
Qwen3.8 Max Preview · Strengths & Key Advantages
- Fenêtre de contexte de 1M et capacité de sortie maximale de 32K tokens (SWE-bench Verified 75.2% · GPQA Diamond 79.5%)
- Débit rapide de 60 tokens/seconde avec une latence au premier token (TTFT) de 680 ms
- Poids ouverts (2.4T total (95B MoE)) permettant un déploiement sur site ou cloud privé via vLLM, SGLang ou Ollama
Considerations & Trade-offs
- Nécessite des serveurs GPU dotés de suffisamment de mémoire VRAM pour l’inférence locale à pleine précision
- Sur les problèmes de recherche fondamentale très complexes, les modèles de raisonnement lourd restent supérieurs
Token Use & Task Economics
Estimated cost and token consumption to complete one representative agentic task.
| Metric | Gemini 4 Argon | Qwen3.8 Max Preview |
|---|---|---|
Cost per Task Estimated USD to finish one task | $2.45 | $0.00 |
Output Tokens / Task Tokens generated per task | 75K | 35K |
Reasoning Tokens / Task Thinking tokens burned per task | 36K | 15K |
Spécifications Techniques
Architecture, release timeline, capabilities, and modalities.
| Caractéristique | Gemini 4 Argon | Qwen3.8 Max Preview |
|---|---|---|
| Official API Model ID (SDK) | models/gemini-4-argon | qwen3-8-max-preview-instruct |
| API Protocol | Google Gemini API (generateContent) + OpenAI-Compatible SDK | OpenAI-Compatible REST / vLLM / SGLang / HuggingFace TGI |
| Release Date | 2026-09 | 2026-07 |
| Knowledge Cutoff | June 2026 | April 2026 |
| Architecture | Pathways Multimodal Sparse Mixture-of-Experts | Colossus-Scale Open Sparse Mixture-of-Experts |
| Parameters | Confidential | 2.4T total (95B active MoE) |
| Enterprise Rate Limits (RPM / TPM) | 10,000 – 30,000 RPM (Tier 5 Enterprise) · 4,000,000 – 10,000,000 TPM | Unlimited (Hardware-bound on self-hosted GPU) / 10,000 RPM Cloud · Unlimited local throughput / 10M+ TPM Managed Cloud |
| Long-Context Tier (>128K/200K) | Standard rate up to 200K tokens; 2x input/output tier above 200K tokens | Free across full context window (only KV-cache VRAM scales) |
| Prompt Cache Policy & TTL | Implicit + Explicit Context Caching at $0.1/1M with configurable TTL | 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 | Supported via external Tavily/Exa Search & E2B/Docker Sandbox tools |
| Official Fine-Tuning Support | Vertex AI Supervised Adapter Tuning & Context Distillation | 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) · Audio (~25–32 tokens/sec) · Video (~258–300 tokens/sec) | Text BPE (~4 chars/token) · Vision (~258–765 tokens per image tile) |
| Quantization & Min GPU VRAM | Cloud-Hosted Proprietary (FP8 / BF16 Tensor Core Cluster) · Zero Local VRAM Required (100% Serverless Cloud API) | BF16 / FP16, FP8 Dynamic, INT4 AWQ / GPTQ, GGUF (Q4_K_M / Q8_0) · 1488 GB VRAM (4-bit Quantized) · 5160 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) | SOC2 Type II · GDPR · Zero Data Retention (ZDR) & HIPAA BAA Available on Enterprise Tier | 100% Air-Gapped On-Premises / Private VPC · Full HIPAA, GDPR & SOC2 Control |
| Input Modalities | text, vision, audio, video | text, vision |
| Output Modalities | text | text |
Gemini 4 Argon vs Qwen3.8 Max Preview: Réponses Rapides (FAQ)
Which is more intelligent, Gemini 4 Argon or Qwen3.8 Max Preview?
Gemini 4 Argon is more intelligent. It scores 97 compared with 93 on the composite Intelligence Index.
Which model is faster?
Gemini 4 Argon is faster, generating 115 tokens per second compared with 60 tokens per second.
Which model is cheaper to run?
Qwen3.8 Max Preview is cheaper at $0.00 per 1M tokens blended, compared with $1.47 (7:2:1 cache:input:output ratio).
Which model has lower latency?
Qwen3.8 Max Preview has lower latency, with a time to first token of 0.68s compared with 0.85s.
Verdict
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., or choose Qwen3.8 Max Preview for conçu pour l’auto-hébergement sur infrastructure privée, le fine-tuning et la souveraineté des données, l’intégration rag et les pipelines de production évolutifs..
Questions fréquentes
Which is cheaper: Gemini 4 Argon or Qwen3.8 Max Preview?+
Gemini 4 Argon costs $2/1M input and $10/1M output tokens, while Qwen3.8 Max Preview costs $0/1M input and $0/1M output tokens.
Which model has a larger context window: Gemini 4 Argon or Qwen3.8 Max Preview?+
Gemini 4 Argon supports 1M tokens whereas Qwen3.8 Max Preview 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: Google & Alibaba Official Documentation