Qwen3.8 Max Preview vs GPT-6 Sol
Benchmarks, API Token Pricing, Context Window & Speed Comparison
Related topics (4)
Compare Qwen3.8 Max Preview by Alibaba against GPT-6 Sol by OpenAI 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.
Qwen3.8 Max Preview
Modèle phare open source de 2.4TEpochal 2.4-billions paramètre à poids ouverts phare combining 1M contexte avec multimodal raisonnement et true de pointe agent autonomy.
$0.00 / 1M
$0.00 / 1M
1M tokens
75.2%
GPT-6 Sol
GPT-6 économiqueun GPT-6 palier offrant complexe codage et professionnel travail à half le prix de GPT-5.6 Sol.
$2.00 / 1M
$10.00 / 1M
1M tokens
82.1%
Différences Clés en un Coup d’Œil
- Qwen3.8 Max Preview is 1.0x cheaper for API usage (input + output tokens combined).
- GPT-6 Sol is faster at 116 tok/s vs 60 tok/s.
- GPT-6 Sol leads on SWE-bench coding (82.1% vs 75.2%).
| Caractéristique | Qwen3.8 Max Preview | GPT-6 Sol |
|---|---|---|
Intelligence Index Composite reasoning & knowledge score (0-100) | 93 | 93 |
Provider | Alibaba | OpenAI |
Input Price Cost per million input tokens | $0.00 / 1M | $2.00 / 1M |
Output Price Cost per million output tokens | $0.00 / 1M | $10.00 / 1M |
Blended Price Weighted 7:2:1 cache:input:output per 1M | $0.00 / 1M | $1.47 / 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 | 32K | 128K |
Throughput Speed Tokens generated per second | 60 tok/s | 116 tok/s |
SWE-bench Verified Real-world software engineering | 75.2% | 82.1% |
GPQA Diamond Graduate-level reasoning | 79.5% | 80.6% |
Cached Input Price Prompt caching discount per 1M tokens | $0.00 / 1M | $0.10 / 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.
Qwen3.8 Max Preview · Strengths & Key Advantages
- monde-record à poids ouverts modèle échelle (2.4T total avec 95B actif inférence)
- Chatbot Arena Elo de 1485 rivaling propriétaire fermé de pointe modèles
- True souverain ouvert modèle avec uncompromised weights et zero censorship
Considerations & Trade-offs
- Enormous matériel empreinte nécessite d'entreprise datacenters ou spécialisé cloud hosting
- Quantized INT4 weights still require sur 600GB de distributed VRAM
GPT-6 Sol · Strengths & Key Advantages
- Roughly 50% cheaper que GPT-5.6 Sol
- Error rate close à half du previous génération
- solide codage agentique et professionnel flux de travail
- Roughly half le prix de GPT-5.6 Sol avec meilleur error rates
Considerations & Trade-offs
- Weaker que GPT-6 Astra sur le hardest tâches
- Sits close dans prix à GPT-6.1 Sol, which est stronger
Token Use & Task Economics
Estimated cost and token consumption to complete one representative agentic task.
| Metric | Qwen3.8 Max Preview | GPT-6 Sol |
|---|---|---|
Cost per Task Estimated USD to finish one task | $0.00 | $2.00 |
Output Tokens / Task Tokens generated per task | 35K | 62K |
Reasoning Tokens / Task Thinking tokens burned per task | 15K | 36K |
Spécifications Techniques
Architecture, release timeline, capabilities, and modalities.
| Caractéristique | Qwen3.8 Max Preview | GPT-6 Sol |
|---|---|---|
| Official API Model ID (SDK) | qwen3-8-max-preview-instruct | gpt-6-sol |
| API Protocol | OpenAI-Compatible REST / vLLM / SGLang / HuggingFace TGI | OpenAI Responses API & Chat Completions v1 |
| Release Date | 2026-07 | 2026-09 |
| Knowledge Cutoff | April 2026 | April 2026 |
| Architecture | Colossus-Scale Open Sparse Mixture-of-Experts | Cost-efficient frontier unified reasoning model |
| Parameters | 2.4T total (95B active MoE) | Confidential |
| Enterprise Rate Limits (RPM / TPM) | Unlimited (Hardware-bound on self-hosted GPU) / 10,000 RPM Cloud · Unlimited local throughput / 10M+ TPM Managed Cloud | 10,000 – 30,000 RPM (Tier 5 Enterprise) · 4,000,000 – 10,000,000 TPM |
| Long-Context Tier (>128K/200K) | Free across full context window (only KV-cache VRAM scales) | Flat rate ($2/1M in) across full 1000K window |
| Prompt Cache Policy & TTL | Automatic prefix KV-caching in vLLM / SGLang (0 cost) | Automatic prefix caching at $0.1/1M (no extra cache-write fee) |
| 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 | Supported via external Tavily/Exa Search & E2B/Docker Sandbox tools | Native Web Search tool + Hosted Code Interpreter & File Search RAG |
| Official Fine-Tuning Support | Full Weights SFT, LoRA, QLoRA, DPO & GRPO RL (Unsloth / Axolotl / Llama-Factory) | Supervised Fine-Tuning (SFT), Direct Preference (DPO) & Reinforcement Fine-Tuning (RFT) |
| 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) · 1488 GB VRAM (4-bit Quantized) · 5160 GB VRAM (Native BF16) | Cloud-Hosted Proprietary (FP8 / BF16 Tensor Core Cluster) · Zero Local VRAM Required (100% Serverless Cloud API) |
| Safety & Frontier Classification | Open-Weights System Card + Customizable Guardrail Layer (Llama Guard / ShieldGemma) | OpenAI Preparedness Framework Audited & System Card Published |
| Enterprise Compliance (ZDR/HIPAA/SOC2) | 100% Air-Gapped On-Premises / Private VPC · Full HIPAA, GDPR & SOC2 Control | SOC2 Type II · GDPR · Zero Data Retention (ZDR) & HIPAA BAA Available on Enterprise Tier |
| Input Modalities | text, vision | text, vision |
| Output Modalities | text | text |
Qwen3.8 Max Preview vs GPT-6 Sol: Réponses Rapides (FAQ)
Which model is faster?
GPT-6 Sol is faster, generating 116 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 1.16s.
Verdict
Choose Qwen3.8 Max Preview for national recherche centers, souverain d'entreprise clouds, et de pointe ai recherche, or choose GPT-6 Sol for coût-sensitive de pointe travail et codage agentique.
Questions fréquentes
Which is cheaper: Qwen3.8 Max Preview or GPT-6 Sol?+
Qwen3.8 Max Preview costs $0/1M input and $0/1M output tokens, while GPT-6 Sol costs $2/1M input and $10/1M output tokens.
Which model has a larger context window: Qwen3.8 Max Preview or GPT-6 Sol?+
Qwen3.8 Max Preview supports 1M tokens whereas GPT-6 Sol 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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Les scores de benchmarks, tarifs par token et limites évoluent rapidement. Vérifiez toujours les spécifications actuelles sur la documentation officielle du fournisseur.
Sources: Alibaba & OpenAI Official Documentation