Qwen Max vs Reka Core
Benchmarks, API Token Pricing, Context Window & Speed Comparison
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Qwen Max by Alibaba and Reka Core by Reka 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, Qwen Max holds a notable pricing advantage for token ingestion. On output generation, Qwen Max is more economical per million generated tokens. In data-heavy architectures requiring extensive repository or document analysis, Reka Core provides an expanded memory horizon with 128K tokens. In latency-sensitive production environments, Reka Core 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.
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Qwen Max
Modelo de Raciocínio ProfundoO Qwen Max da Alibaba é um modelo de inteligência artificial com janela de contexto de 32K tokens e preço de API de $1.6/1M na entrada e $6.4/1M na saída, projetado para programação, raciocínio e automação empresarial.
$1.60 / 1M
$6.40 / 1M
32.8K tokens
32.5%
Reka Core
Modelo de Raciocínio ProfundoO Reka Core da Reka é um modelo de inteligência artificial com janela de contexto de 128K tokens e preço de API de $3/1M na entrada e $15/1M na saída, projetado para programação, raciocínio e automação empresarial.
$3.00 / 1M
$15.00 / 1M
128K tokens
N/A
Principais Diferenças em Resumo
- Qwen Max is 2.2x cheaper for API usage (input + output tokens combined).
- Reka Core has a larger context window (128K vs 33K tokens).
- Reka Core is faster at 75 tok/s vs 65 tok/s.
| Feature | Qwen Max | Reka Core |
|---|---|---|
Intelligence Index Composite reasoning & knowledge score (0-100) | 78 | 77 |
Provider | Alibaba | Reka |
Input Price Cost per million input tokens | $1.60 / 1M | $3.00 / 1M |
Output Price Cost per million output tokens | $6.40 / 1M | $15.00 / 1M |
Blended Price Weighted 7:2:1 cache:input:output per 1M | $1.07 / 1M | $2.31 / 1M |
Context Window Maximum input + output tokens | 32.8K tokens~49 A4 pages of text | 128K tokens~192 A4 pages of text |
Max Output Tokens Per single request | 8K | 8K |
Throughput Speed Tokens generated per second | 65 tok/s | 75 tok/s |
SWE-bench Verified Real-world software engineering | 32.5% | N/A |
GPQA Diamond Graduate-level reasoning | 48% | N/A |
Cached Input Price Prompt caching discount per 1M tokens | $0.16 / 1M | $0.30 / 1M |
Quem vence em cada categoria?
At-a-glance decision frameworkEvaluations ongoing
Pontuações de Benchmarks Verificadas
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.
Tokens emitted per second during active generation streaming.
Latência e Tempo de Resposta
Time to first token (TTFT) and estimated end-to-end response time for a typical 500-token response.
Simulador Interativo de Custo de Tokens
Adjust sliders to calculate projected monthly cloud API spending with prompt caching discounts.
Qwen Max · Strengths & Key Advantages
- Arquitetura Dense Autoregressive Transformer with Multi-Head Attention avançada da Alibaba com contexto de 32K tokens
- Excelente relação custo-benefício ($1.6/1M entrada, $6.4/1M saída) e baixa latência
- Forte desempenho em benchmarks de código, análise lógica e tarefas multilíngues
Considerations & Trade-offs
- Cargas de trabalho com contexto máximo exigem monitoramento de custos de tokens
- Requer engenharia de prompt adequada para fluxos de trabalho altamente específicos
Reka Core · Strengths & Key Advantages
- Arquitetura Multimodal Foundation Omnimodal Transformer avançada da Reka com contexto de 128K tokens
- Excelente relação custo-benefício ($3/1M entrada, $15/1M saída) e baixa latência
- Forte desempenho em benchmarks de código, análise lógica e tarefas multilíngues
Considerations & Trade-offs
- Cargas de trabalho com contexto máximo exigem monitoramento de custos de tokens
- Requer engenharia de prompt adequada para fluxos de trabalho altamente específicos
Token Use & Task Economics
Estimated cost and token consumption to complete one representative agentic task.
| Metric | Qwen Max | Reka Core |
|---|---|---|
Cost per Task Estimated USD to finish one task | $0.18 | $0.22 |
Output Tokens / Task Tokens generated per task | 18K | 15K |
Reasoning Tokens / Task Thinking tokens burned per task | 6K | 3K |
Especificações Técnicas
Architecture, release timeline, capabilities, and modalities.
| Feature | Qwen Max | Reka Core |
|---|---|---|
| Official API Model ID (SDK) | qwen-max-instruct | reka-core |
| API Protocol | Alibaba REST API (OpenAI-Compatible) | Reka REST API (OpenAI-Compatible) |
| Release Date | 2024-04 | 2024-04 |
| Knowledge Cutoff | September 2023 | December 2023 |
| Architecture | Dense Autoregressive Transformer with Multi-Head Attention | Multimodal Foundation Omnimodal Transformer |
| Parameters | Confidential | Confidential |
| Enterprise Rate Limits (RPM / TPM) | 5,000 – 15,000 RPM (Production Tier) · 800,000 – 2,000,000 TPM | 5,000 – 15,000 RPM (Production Tier) · 800,000 – 2,000,000 TPM |
| Long-Context Tier (>128K/200K) | Flat rate ($1.6/1M in) across full 33K window | Flat rate ($3/1M in) across full 128K window |
| Prompt Cache Policy & TTL | Cached input at $0.16/1M tokens | Cached input at $0.3/1M tokens |
| Computer Use / GUI Agent | API / CLI Tool-Use & DOM Agent ready (no raw pixel desktop click loop) | API / CLI Tool-Use & DOM Agent ready (no raw pixel desktop click loop) |
| 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 | API Prompt Engineering & RAG Distillation | API Prompt Engineering & RAG 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 | Alibaba Production Safety & Alignment Verified | Reka Production Safety & Alignment 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 |
Qwen Max vs Reka Core: Respostas Rápidas (FAQ)
Which is more intelligent, Qwen Max or Reka Core?
Qwen Max is more intelligent. It scores 78 compared with 77 on the composite Intelligence Index.
Which model is faster?
Reka Core is faster, generating 75 tokens per second compared with 65 tokens per second.
Which model is cheaper to run?
Qwen Max is cheaper at $1.07 per 1M tokens blended, compared with $2.31 (7:2:1 cache:input:output ratio).
Which model has a larger context window?
Reka Core has a larger context window, supporting 128K tokens compared with 32.8K tokens.
Veredito
Verdict & Model Selection Recommendation: Choose Qwen Max if your priority is Multilingual enterprise chat, customer support, and general NLP automation. Its performance in demanding tasks justifies its operational token cost. Choose Reka Core if your workflow prioritizes Multimodal video search, audio-visual QA, and cross-format media intelligence. For large-scale batch processing or customer-facing conversational interfaces, its balance of speed and economics delivers superior ROI.
Perguntas Frequentes (FAQ)
How does Qwen Max compare to Reka Core in API token pricing and cost per token?+
Qwen Max costs $1.60 per 1M input tokens and $6.40 per 1M output tokens, whereas Reka Core costs $3.00 input and $15.00 output. Overall, Qwen Max is the more cost-effective model for high-volume API pipelines. Both models support prompt caching discounts of up to 90% for repeated prompt prefixes.
Which model has a larger context window: Qwen Max or Reka Core?+
Qwen Max supports a context window of 33K tokens (~25k words), while Reka Core supports 128K tokens (~96k words). Therefore, Reka Core can process more textual data and document context in a single prompt.
Which AI model is better for software engineering and coding: Qwen Max or Reka Core?+
Qwen Max scores 32.5% on SWE-bench Verified, while Reka Core scores competitively for software generation. For complex autonomous debugging and agentic engineering, Qwen Max provides robust code generation and tool calling.
What is the difference in reasoning capability and extended thinking between Qwen Max and Reka Core?+
Qwen Max features native chain-of-thought extended thinking mode with configurable reasoning effort. In contrast, Reka Core provides chain-of-thought reasoning depth for complex math and science.
How does inference speed (tokens per second) compare between Qwen Max and Reka Core?+
Qwen Max generates approximately 65 tokens per second, while Reka Core delivers 75 tokens per second. In latency-sensitive production environments, Reka Core provides faster time-to-first-token (TTFT) and higher streaming response velocity.
Can you implement a tiered LLM routing setup combining Qwen Max and Reka Core to optimize costs?+
Yes! High-efficiency architectures route high-frequency routine classification and conversational tasks to Qwen Max to save operational costs, and dynamically escalate complex architectural queries or reasoning tasks to Reka Core. This tiered routing pattern cuts overall API bills by up to 70%.
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.
Fontes oficiais: GetInfoUs Independent AI Benchmarks & Official API Specs