Llama 5 vs Qwen3.8 Max Preview
Benchmarks, API Token Pricing, Context Window & Speed Comparison
Related topics (12)
Llama 5 by Meta and Qwen3.8 Max Preview by Alibaba 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, Llama 5 provides an expanded memory horizon with 5,000K tokens. In latency-sensitive production environments, Llama 5 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.
Beliebige Modelle wechseln & vergleichen
Select any AI models from our verified benchmark database to generate live side-by-side matrices.
Llama 5
Open-Weights-ModellLlama 5 von Meta ist ein leistungsstarkes KI-Modell mit einem Kontextfenster von 5M Token zu API-Preisen von $0/1M Input- und $0/1M Output-Token für Softwareentwicklung, Analyse und Enterprise-Workflows.
$0.00 / 1M
$0.00 / 1M
5M tokens
74%
Qwen3.8 Max Preview
Open-Weights-ModellQwen3.8 Max Preview von Alibaba ist ein leistungsstarkes KI-Modell mit einem Kontextfenster von 1M Token zu API-Preisen von $2.0/1M Input- und $6.0/1M Output-Token für Softwareentwicklung, Analyse und Enterprise-Workflows.
$0.00 / 1M
$0.00 / 1M
1M tokens
75.2%
Wichtigste Unterschiede auf einen Blick
- Both models cost the same under this workload — choose on capability, not price.
- Llama 5 has a larger context window (5M vs 1M tokens).
- Llama 5 is faster at 65 tok/s vs 60 tok/s.
- Qwen3.8 Max Preview leads on SWE-bench coding (75.2% vs 74.0%).
| Feature | Llama 5 | Qwen3.8 Max Preview |
|---|---|---|
Intelligence Index Composite reasoning & knowledge score (0-100) | 94 | 93 |
Provider | Meta | Alibaba |
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 | 5M tokens~7,500 A4 pages of text | 1M tokens~1,500 A4 pages of text |
Max Output Tokens Per single request | 128K | 32K |
Throughput Speed Tokens generated per second | 65 tok/s | 60 tok/s |
SWE-bench Verified Real-world software engineering | 74% | 75.2% |
GPQA Diamond Graduate-level reasoning | 84% | 79.5% |
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.
Contamination-free competitive programming problems collected continuously over time.
American Invitational Mathematics Examination — multi-step Olympiad-level reasoning.
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.
Llama 5 · Strengths & Key Advantages
- Fortschrittliche Mixture-of-Experts with recursive self-improvement (~600B total, 40-80B active)-Architektur von Meta mit 5M Token Kontextfenster
- Starkes Preis-Leistungs-Verhältnis ($0/1M Input, $0/1M Output) bei hoher Durchsatzrate
- Hohe Präzision bei Code-Generierung, logischem Schließen und mehrsprachigen Benchmarks
Considerations & Trade-offs
- Bei sehr großen Kontextmengen ist eine sorgfältige Token-Budgetierung ratsam
- Spezialisierte Fachaufgaben profitieren von gezieltem Prompt-Engineering
Qwen3.8 Max Preview · Strengths & Key Advantages
- Fortschrittliche Colossus-Scale Open Sparse Mixture-of-Experts-Architektur von Alibaba mit 1M Token Kontextfenster
- Starkes Preis-Leistungs-Verhältnis ($2.0/1M Input, $6.0/1M Output) bei hoher Durchsatzrate
- Hohe Präzision bei Code-Generierung, logischem Schließen und mehrsprachigen Benchmarks
Considerations & Trade-offs
- Bei sehr großen Kontextmengen ist eine sorgfältige Token-Budgetierung ratsam
- Spezialisierte Fachaufgaben profitieren von gezieltem Prompt-Engineering
Token Use & Task Economics
Estimated cost and token consumption to complete one representative agentic task.
| Metric | Llama 5 | Qwen3.8 Max Preview |
|---|---|---|
Cost per Task Estimated USD to finish one task | $0.00 | $0.00 |
Output Tokens / Task Tokens generated per task | 40K | 35K |
Reasoning Tokens / Task Thinking tokens burned per task | 20K | 15K |
Technische Spezifikationen
Architecture, release timeline, capabilities, and modalities.
| Feature | Llama 5 | Qwen3.8 Max Preview |
|---|---|---|
| Official API Model ID (SDK) | meta-llama/Llama-5 | qwen3-8-max-preview-instruct |
| API Protocol | OpenAI-Compatible REST / vLLM / SGLang / HuggingFace TGI | OpenAI-Compatible REST / vLLM / SGLang / HuggingFace TGI |
| Release Date | 2026-04 | 2026-07 |
| Knowledge Cutoff | January 2026 | April 2026 |
| Architecture | Mixture-of-Experts with recursive self-improvement (~600B total, 40-80B active) | Colossus-Scale Open Sparse Mixture-of-Experts |
| Parameters | 600B+ total (~40-80B active) | 2.4T total (95B active MoE) |
| 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) | 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 | Supported via external Tavily/Exa Search & E2B/Docker Sandbox tools |
| 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) · 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 | BF16 / FP16, FP8 Dynamic, INT4 AWQ / GPTQ, GGUF (Q4_K_M / Q8_0) · 372 GB VRAM (4-bit Quantized) · 1290 GB VRAM (Native BF16) | 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 | Open-Weights System Card + Customizable Guardrail Layer (Llama Guard / ShieldGemma) | Open-Weights System Card + Customizable Guardrail Layer (Llama Guard / ShieldGemma) |
| 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, video, audio | text, vision |
| Output Modalities | text | text |
Llama 5 vs Qwen3.8 Max Preview: Schnelle Antworten (FAQ)
Which is more intelligent, Llama 5 or Qwen3.8 Max Preview?
Llama 5 is more intelligent. It scores 94 compared with 93 on the composite Intelligence Index.
Which model is faster?
Llama 5 is faster, generating 65 tokens per second compared with 60 tokens per second.
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.
Which model has a larger context window?
Llama 5 has a larger context window, supporting 5M tokens compared with 1M tokens.
Fazit
Verdict & Model Selection Recommendation: Choose Llama 5 if your priority is Autonomous open-source multi-agent systems, deep software refactoring, and frontier research. Its performance in demanding tasks justifies its operational token cost. Choose Qwen3.8 Max Preview if your workflow prioritizes National research centers, sovereign enterprise clouds, and frontier AI research. For large-scale batch processing or customer-facing conversational interfaces, its balance of speed and economics delivers superior ROI.
Häufig gestellte Fragen (FAQ)
How does Llama 5 compare to Qwen3.8 Max Preview in API token pricing and cost per token?+
Both Llama 5 and Qwen3.8 Max Preview 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: Llama 5 or Qwen3.8 Max Preview?+
Llama 5 supports a context window of 5,000K tokens (~3,750k words), while Qwen3.8 Max Preview supports 1,000K tokens (~750k words). Therefore, Llama 5 can process more textual data and document context in a single prompt.
Which AI model is better for software engineering and coding: Llama 5 or Qwen3.8 Max Preview?+
Llama 5 scores 74% on SWE-bench Verified while Qwen3.8 Max Preview scores 75.2%. For autonomous software engineering and multi-file debugging, Qwen3.8 Max Preview holds the verified benchmark lead.
What is the difference in reasoning capability and extended thinking between Llama 5 and Qwen3.8 Max Preview?+
Llama 5 features native chain-of-thought extended thinking mode with configurable reasoning effort. In contrast, Qwen3.8 Max Preview provides chain-of-thought reasoning depth for complex math and science.
How does inference speed (tokens per second) compare between Llama 5 and Qwen3.8 Max Preview?+
Llama 5 generates approximately 65 tokens per second, while Qwen3.8 Max Preview delivers 60 tokens per second. In latency-sensitive production environments, Llama 5 provides faster time-to-first-token (TTFT) and higher streaming response velocity.
Can you implement a tiered LLM routing setup combining Llama 5 and Qwen3.8 Max Preview 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 Llama 5, and route deep architectural or complex reasoning queries to Llama 5. 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
- Gemini 3.8 Flash vs Llama 5: Benchmarks, Pricing & Speed ComparisonVergleich
- GPT-OSS 120B vs Llama 5: Benchmarks, Pricing & Speed ComparisonVergleich
- Llama 4 Behemoth vs Llama 5: Benchmarks, Pricing & Speed ComparisonVergleich
- Llama 5 vs DeepSeek V4 Pro: Benchmarks, Pricing & Speed ComparisonVergleich
- Llama 5 vs GPT-6 Astra: Benchmarks, Pricing & Speed ComparisonVergleich
- Llama 5 vs Kimi K3: Benchmarks, Pricing & Speed ComparisonVergleich
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: GetInfoUs Independent AI Benchmarks & Official API Specs