Qwen-VL Max vs Reka Core
Benchmarks, API Token Pricing, Context Window & Speed Comparison
Related topics (12)
Qwen-VL 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-VL Max holds a notable pricing advantage for token ingestion. On output generation, Qwen-VL Max is more economical per million generated tokens. In data-heavy architectures requiring extensive repository or document analysis, Qwen-VL Max provides an expanded memory horizon with 131K tokens. In latency-sensitive production environments, both models deliver balanced throughput and low time-to-first-token latency for interactive 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-VL Max
Model Penalaran MendalamQwen-VL Max dari Alibaba adalah model AI canggih dengan jendela konteks 131K token serta harga API $0.7/1M token input dan $2.8/1M token output untuk pemrograman, penalaran, dan otomasi bisnis.
$0.70 / 1M
$2.80 / 1M
131.1K tokens
N/A
Reka Core
Model Penalaran MendalamReka Core dari Reka adalah model AI canggih dengan jendela konteks 128K token serta harga API $3/1M token input dan $15/1M token output untuk pemrograman, penalaran, dan otomasi bisnis.
$3.00 / 1M
$15.00 / 1M
128K tokens
N/A
Perbedaan Utama Sekilas
- Qwen-VL Max is 5.0x cheaper for API usage (input + output tokens combined).
- Qwen-VL Max has a larger context window (131K vs 128K tokens).
- Reka Core is faster at 75 tok/s vs 75 tok/s.
| Feature | Qwen-VL Max | Reka Core |
|---|---|---|
Intelligence Index Composite reasoning & knowledge score (0-100) | 76 | 77 |
Provider | Alibaba | Reka |
Input Price Cost per million input tokens | $0.70 / 1M | $3.00 / 1M |
Output Price Cost per million output tokens | $2.80 / 1M | $15.00 / 1M |
Blended Price Weighted 7:2:1 cache:input:output per 1M | $0.47 / 1M | $2.31 / 1M |
Context Window Maximum input + output tokens | 131.1K tokens~197 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 | 75 tok/s | 75 tok/s |
SWE-bench Verified Real-world software engineering | N/A | N/A |
GPQA Diamond Graduate-level reasoning | 46.5% | N/A |
Cached Input Price Prompt caching discount per 1M tokens | $0.07 / 1M | $0.30 / 1M |
Siapa Unggul di Mana?
At-a-glance decision frameworkEvaluations ongoing
Skor Benchmark Terverifikasi
Official evaluation metrics verified by independent third-party research labs.
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.
Latensi & Waktu Respons
Time to first token (TTFT) and estimated end-to-end response time for a typical 500-token response.
Simulator Biaya Token Interaktif
Adjust sliders to calculate projected monthly cloud API spending with prompt caching discounts.
Qwen-VL Max · Strengths & Key Advantages
- Arsitektur Multimodal Vision-Language Transformer mutakhir dari Alibaba dengan dukungan konteks 131K token
- Efisiensi biaya API yang kompetitif ($0.7/1M input, $2.8/1M output) dan respons cepat
- Performa andal dalam pembuatan kode, analisis logika, serta pemahaman multibahasa
Considerations & Trade-offs
- Penggunaan konteks berukuran penuh memerlukan pengelolaan anggaran token yang cermat
- Membutuhkan perancangan prompt yang tepat untuk kasus penggunaan industri spesifik
Reka Core · Strengths & Key Advantages
- Arsitektur Multimodal Foundation Omnimodal Transformer mutakhir dari Reka dengan dukungan konteks 128K token
- Efisiensi biaya API yang kompetitif ($3/1M input, $15/1M output) dan respons cepat
- Performa andal dalam pembuatan kode, analisis logika, serta pemahaman multibahasa
Considerations & Trade-offs
- Penggunaan konteks berukuran penuh memerlukan pengelolaan anggaran token yang cermat
- Membutuhkan perancangan prompt yang tepat untuk kasus penggunaan industri spesifik
Token Use & Task Economics
Estimated cost and token consumption to complete one representative agentic task.
| Metric | Qwen-VL Max | Reka Core |
|---|---|---|
Cost per Task Estimated USD to finish one task | $0.15 | $0.22 |
Output Tokens / Task Tokens generated per task | 16K | 15K |
Reasoning Tokens / Task Thinking tokens burned per task | 5K | 3K |
Spesifikasi Teknis
Architecture, release timeline, capabilities, and modalities.
| Feature | Qwen-VL Max | Reka Core |
|---|---|---|
| Official API Model ID (SDK) | qwen-vl-max-instruct | reka-core |
| API Protocol | Alibaba REST API (OpenAI-Compatible) | Reka REST API (OpenAI-Compatible) |
| Release Date | 2024-04 | 2024-04 |
| Knowledge Cutoff | December 2023 | December 2023 |
| Architecture | Multimodal Vision-Language Transformer | 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 ($0.7/1M in) across full 131K window | Flat rate ($3/1M in) across full 128K window |
| Prompt Cache Policy & TTL | Cached input at $0.07/1M tokens | Cached input at $0.3/1M tokens |
| Computer Use / GUI Agent | Supported (Screen coordinate grounding, browser & desktop GUI action loops) | 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) · Vision (~258–765 tokens per image tile) | 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, vision | text, vision, audio, video |
| Output Modalities | text | text |
Qwen-VL Max vs Reka Core: Jawaban Cepat (FAQ)
Which is more intelligent, Qwen-VL Max or Reka Core?
Reka Core is more intelligent. It scores 77 compared with 76 on the composite Intelligence Index.
Which model is cheaper to run?
Qwen-VL Max is cheaper at $0.47 per 1M tokens blended, compared with $2.31 (7:2:1 cache:input:output ratio).
Which model has lower latency?
Reka Core has lower latency, with a time to first token of 0.58s compared with 0.65s.
Which model has a larger context window?
Qwen-VL Max has a larger context window, supporting 131.1K tokens compared with 128K tokens.
Kesimpulan
Verdict & Model Selection Recommendation: Choose Qwen-VL Max if your priority is Invoice processing, complex document intelligence, visual inspection, and diagram QA. 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.
Pertanyaan Umum (FAQ)
How does Qwen-VL Max compare to Reka Core in API token pricing and cost per token?+
Qwen-VL Max costs $0.70 per 1M input tokens and $2.80 per 1M output tokens, whereas Reka Core costs $3.00 input and $15.00 output. Overall, Qwen-VL 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-VL Max or Reka Core?+
Qwen-VL Max supports a context window of 131K tokens (~98k words), while Reka Core supports 128K tokens (~96k words). Therefore, Qwen-VL Max can process more textual data and document context in a single prompt.
Which AI model is better for software engineering and coding: Qwen-VL Max or Reka Core?+
Qwen-VL Max scores at the frontier of coding benchmarks, while Reka Core scores competitively for software generation. For complex autonomous debugging and agentic engineering, Qwen-VL Max provides robust code generation and tool calling.
What is the difference in reasoning capability and extended thinking between Qwen-VL Max and Reka Core?+
Qwen-VL 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-VL Max and Reka Core?+
Both Qwen-VL Max and Reka Core deliver matching throughput at approximately 75 tokens per second. In latency-sensitive production environments, streaming response velocity is comparable, with real-world time-to-first-token (TTFT) determined primarily by provider routing and concurrency load.
Can you implement a tiered LLM routing setup combining Qwen-VL Max and Reka Core to optimize costs?+
Yes! High-efficiency architectures route high-frequency routine classification and conversational tasks to Qwen-VL 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.
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