Qwen 2.5 Coder 32B vs DeepSeek-Coder-V2 236B
Benchmarks, API Token Pricing, Context Window & Speed Comparison
Related topics (12)
Qwen 2.5 Coder 32B by Alibaba and DeepSeek-Coder-V2 236B by DeepSeek 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, DeepSeek-Coder-V2 236B holds a notable pricing advantage for token ingestion. On output generation, DeepSeek-Coder-V2 236B is more economical per million generated tokens. In data-heavy architectures requiring extensive repository or document analysis, both models provide an identical memory horizon with 128K tokens. In latency-sensitive production environments, Qwen 2.5 Coder 32B 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 2.5 Coder 32B
Modello a Pesi ApertiQwen 2.5 Coder 32B di Alibaba è un modello di intelligenza artificiale con finestra di contesto da 128K token e tariffazione API di $0.2/1M token in input e $0.6/1M in output, ideale per coding, ragionamento e automazione aziendale.
$0.20 / 1M
$0.60 / 1M
128K tokens
48.6%
DeepSeek-Coder-V2 236B
Modello a Pesi ApertiDeepSeek-Coder-V2 236B di DeepSeek è un modello di intelligenza artificiale con finestra di contesto da 128K token e tariffazione API di $0.14/1M token in input e $0.28/1M in output, ideale per coding, ragionamento e automazione aziendale.
$0.14 / 1M
$0.28 / 1M
128K tokens
42.1%
Differenze Chiave a Colpo d’Occhio
- DeepSeek-Coder-V2 236B is 1.9x cheaper for API usage (input + output tokens combined).
- Qwen 2.5 Coder 32B is faster at 78 tok/s vs 74 tok/s.
- Qwen 2.5 Coder 32B leads on SWE-bench coding (48.6% vs 42.1%).
| Feature | Qwen 2.5 Coder 32B | DeepSeek-Coder-V2 236B |
|---|---|---|
Intelligence Index Composite reasoning & knowledge score (0-100) | 43 | 42 |
Provider | Alibaba | DeepSeek |
Input Price Cost per million input tokens | $0.20 / 1M | $0.14 / 1M |
Output Price Cost per million output tokens | $0.60 / 1M | $0.28 / 1M |
Blended Price Weighted 7:2:1 cache:input:output per 1M | $0.13 / 1M | $0.07 / 1M |
Context Window Maximum input + output tokens | 128K tokens~192 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 | 78 tok/s | 74 tok/s |
SWE-bench Verified Real-world software engineering | 48.6% | 42.1% |
GPQA Diamond Graduate-level reasoning | 48.2% | 49.5% |
Cached Input Price Prompt caching discount per 1M tokens | $0.04 / 1M | $0.01 / 1M |
Chi vince in ogni categoria?
At-a-glance decision frameworkPunteggi Benchmark Verificati
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.
Latenza e Tempo di Risposta
Time to first token (TTFT) and estimated end-to-end response time for a typical 500-token response.
Simulatore Interattivo dei Costi Token
Adjust sliders to calculate projected monthly cloud API spending with prompt caching discounts.
Qwen 2.5 Coder 32B · Strengths & Key Advantages
- Architettura Dense Code-Specialized Transformer avanzata di Alibaba con supporto fino a 128K token di contesto
- Ottimo rapporto qualità-prezzo ($0.2/1M input, $0.6/1M output) e bassa latenza
- Elevate prestazioni nei benchmark di programmazione, logica e comprensione multilingue
Considerations & Trade-offs
- L'utilizzo intensivo di contesti molto ampi richiede un attento monitoraggio dei costi dei token
- Per casi d'uso verticali complessi è consigliabile un prompt engineering mirato
DeepSeek-Coder-V2 236B · Strengths & Key Advantages
- Architettura 236B Mixture-of-Experts (21B active) avanzata di DeepSeek con supporto fino a 128K token di contesto
- Ottimo rapporto qualità-prezzo ($0.14/1M input, $0.28/1M output) e bassa latenza
- Elevate prestazioni nei benchmark di programmazione, logica e comprensione multilingue
Considerations & Trade-offs
- L'utilizzo intensivo di contesti molto ampi richiede un attento monitoraggio dei costi dei token
- Per casi d'uso verticali complessi è consigliabile un prompt engineering mirato
Token Use & Task Economics
Estimated cost and token consumption to complete one representative agentic task.
| Metric | Qwen 2.5 Coder 32B | DeepSeek-Coder-V2 236B |
|---|---|---|
Cost per Task Estimated USD to finish one task | $0.18 | $0.14 |
Output Tokens / Task Tokens generated per task | 35K | 30K |
Reasoning Tokens / Task Thinking tokens burned per task | 0K | 0K |
Specifiche Tecniche
Architecture, release timeline, capabilities, and modalities.
| Feature | Qwen 2.5 Coder 32B | DeepSeek-Coder-V2 236B |
|---|---|---|
| Official API Model ID (SDK) | qwen-2-5-coder-32b-instruct | deepseek-chat |
| API Protocol | OpenAI-Compatible REST / vLLM / SGLang / HuggingFace TGI | OpenAI-Compatible REST / vLLM / SGLang / HuggingFace TGI |
| Release Date | 2024-11 | 2024-06 |
| Knowledge Cutoff | September 2024 | December 2023 |
| Architecture | Dense Code-Specialized Transformer | 236B Mixture-of-Experts (21B active) |
| Parameters | 32B | 236B |
| 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) | Flat rate ($0.2/1M in) across full 128K window | Flat rate ($0.14/1M in) across full 128K window |
| Prompt Cache Policy & TTL | Cached input at $0.04/1M tokens | Automatic prefix caching at $0.014/1M (no extra cache-write fee) |
| 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 | 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) | Text BPE (~4 chars/token) |
| Quantization & Min GPU VRAM | BF16 / FP16, FP8 Dynamic, INT4 AWQ / GPTQ, GGUF (Q4_K_M / Q8_0) · 20 GB VRAM (4-bit Quantized) · 69 GB VRAM (Native BF16) | BF16 / FP16, FP8 Dynamic, INT4 AWQ / GPTQ, GGUF (Q4_K_M / Q8_0) · 146 GB VRAM (4-bit Quantized) · 507 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 | text |
| Output Modalities | text | text |
Qwen 2.5 Coder 32B vs DeepSeek-Coder-V2 236B: Risposte Rapide (FAQ)
Which is more intelligent, Qwen 2.5 Coder 32B or DeepSeek-Coder-V2 236B?
Qwen 2.5 Coder 32B is more intelligent. It scores 43 compared with 42 on the composite Intelligence Index.
Which model is faster?
Qwen 2.5 Coder 32B is faster, generating 78 tokens per second compared with 74 tokens per second.
Which model is cheaper to run?
DeepSeek-Coder-V2 236B is cheaper at $0.07 per 1M tokens blended, compared with $0.13 (7:2:1 cache:input:output ratio).
Which model has lower latency?
DeepSeek-Coder-V2 236B has lower latency, with a time to first token of 0.51s compared with 0.54s.
Verdetto
Verdict & Model Selection Recommendation: Choose Qwen 2.5 Coder 32B if your priority is Local IDE completions, CI/CD automated PR reviews, and autonomous coding agents. Its performance in demanding tasks justifies its operational token cost. Choose DeepSeek-Coder-V2 236B if your workflow prioritizes Large codebase refactoring, code translation between legacy and modern stacks, and developer API tools. For large-scale batch processing or customer-facing conversational interfaces, its balance of speed and economics delivers superior ROI.
Domande Frequenti (FAQ)
How does Qwen 2.5 Coder 32B compare to DeepSeek-Coder-V2 236B in API token pricing and cost per token?+
Qwen 2.5 Coder 32B costs $0.20 per 1M input tokens and $0.60 per 1M output tokens, whereas DeepSeek-Coder-V2 236B costs $0.14 input and $0.28 output. Overall, DeepSeek-Coder-V2 236B 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 2.5 Coder 32B or DeepSeek-Coder-V2 236B?+
Both Qwen 2.5 Coder 32B and DeepSeek-Coder-V2 236B offer an identical context window of 128K tokens (~96k words). Both architectures provide equal capacity for large document ingestion, multi-file codebases, and extended multi-turn conversation histories.
Which AI model is better for software engineering and coding: Qwen 2.5 Coder 32B or DeepSeek-Coder-V2 236B?+
Qwen 2.5 Coder 32B scores 48.6% on SWE-bench Verified while DeepSeek-Coder-V2 236B scores 42.1%. For autonomous software engineering and multi-file debugging, Qwen 2.5 Coder 32B holds the verified benchmark lead.
What is the difference in reasoning capability and extended thinking between Qwen 2.5 Coder 32B and DeepSeek-Coder-V2 236B?+
Qwen 2.5 Coder 32B is optimized for low-latency direct generation. In contrast, DeepSeek-Coder-V2 236B focuses on standard fast inference.
How does inference speed (tokens per second) compare between Qwen 2.5 Coder 32B and DeepSeek-Coder-V2 236B?+
Qwen 2.5 Coder 32B generates approximately 78 tokens per second, while DeepSeek-Coder-V2 236B delivers 74 tokens per second. In latency-sensitive production environments, Qwen 2.5 Coder 32B provides faster time-to-first-token (TTFT) and higher streaming response velocity.
Can you implement a tiered LLM routing setup combining Qwen 2.5 Coder 32B and DeepSeek-Coder-V2 236B to optimize costs?+
Yes! High-efficiency architectures route high-frequency routine classification and conversational tasks to DeepSeek-Coder-V2 236B to save operational costs, and dynamically escalate complex architectural queries or reasoning tasks to Qwen 2.5 Coder 32B. 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.
Fonti ufficiali: GetInfoUs Independent AI Benchmarks & Official API Specs