Head-to-Head Model Comparison

Qwen3-Coder 30B-A3B Instruct vs Qwen 2.5 Coder 32B

Benchmarks, API Token Pricing, Context Window & Speed Comparison

Geprüft vom GetInfoUs-ForschungsteamZuletzt geprüft: October 2026Redaktionelle Richtlinien

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Qwen3-Coder 30B-A3B InstructProfile

Qwen 2.5 Coder 32BProfile

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5-Model Matrix Workbench

Popular Matchups:

Alibaba

Qwen3-Coder 30B-A3B Instruct

Open-Weights-Basismodell

Qwen3-Coder 30B-A3B Instruct von Alibaba ist ein KI-Modell für Self-Hosting im eigenen Rechenzentrum, Fine-Tuning und volle Datensouveränität mit 262K Kontextfenster, 140 Token/s Durchsatz, einem Intelligenz-Index von 76/100 und kostenlosem

Intelligence Index76 / 100

Input Token:

$0.00 / 1M

Output Token:

$0.00 / 1M

Context Window:

262.1K tokens

SWE-bench Verified:

N/A

Alibaba

Qwen 2.5 Coder 32B

Open-Weights-Coding-Spezialist

Qwen 2.5 Coder 32B von Alibaba ist ein KI-Modell für lokale Softwareentwicklung und Codegenerierung auf Basis offener Modellgewichte mit 128K Kontextfenster, 78 Token/s Durchsatz, einem Intelligenz-Index von 43/100 und $0.2/$0.6 pro 1M Toke

Intelligence Index43 / 100

Input Token:

$0.20 / 1M

Output Token:

$0.60 / 1M

Context Window:

128K tokens

SWE-bench Verified:

48.6%

Wichtigste Unterschiede auf einen Blick

Qwen3-Coder 30B-A3B Instruct is 1.0x cheaper for API usage (input + output tokens combined).
Qwen3-Coder 30B-A3B Instruct has a larger context window (262K vs 128K tokens).
Qwen3-Coder 30B-A3B Instruct is faster at 140 tok/s vs 78 tok/s.

Need definitions for token pricing, context, or benchmarks?Explore AI Glossary →

Feature	Qwen3-Coder 30B-A3B Instruct	Qwen 2.5 Coder 32B
Intelligence Index Composite reasoning & knowledge score (0-100)	76	43
Provider	Alibaba	Alibaba
Input Price Cost per million input tokens	$0.00 / 1M	$0.20 / 1M
Output Price Cost per million output tokens	$0.00 / 1M	$0.60 / 1M
Blended Price Weighted 7:2:1 cache:input:output per 1M	$0.00 / 1M	$0.13 / 1M
Context Window Maximum input + output tokens	262.1K tokens~393 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	140 tok/s	78 tok/s
SWE-bench Verified Real-world software engineering	N/A	48.6%
GPQA Diamond Graduate-level reasoning	N/A	48.2%
Cached Input Price Prompt caching discount per 1M tokens	$0.00 / 1M	$0.04 / 1M

Wer gewinnt in welcher Disziplin?

At-a-glance decision framework

Coding

Evaluations ongoing

Cost

Qwen3-Coder 30B-A3B Instruct

1.0x cheaper monthly runtime

Speed

Qwen3-Coder 30B-A3B Instruct

+79% faster generation

Context

Qwen3-Coder 30B-A3B Instruct

262.1K tokens window

Geprüfte Benchmark-Ergebnisse im Vergleich

Official evaluation metrics verified by independent third-party research labs.

Qwen3-Coder 30B-A3B InstructQwen 2.5 Coder 32B

SWE-bench Verified (Autonomous Coding)

Industry benchmark evaluating real-world GitHub issue resolution and multi-file debugging.

N/Avs48.6%

GPQA Diamond (Doctorate-Level Science & Math)

Challenging questions created by domain experts to test complex PhD-level reasoning.

N/Avs48.2%

MMLU-Pro (Multi-task Language Understanding)

Hardened reasoning and academic knowledge benchmark with reduced lucky-guess probability.

74%vs68.9%

LMSYS Chatbot Arena Elo

Crowdsourced human preference evaluation based on blind, head-to-head model battles.

1285ptsvs1268pts

Humanity's Last Exam (Frontier Expert Knowledge)

Extremely difficult, closed-ended questions spanning the frontier of human expert knowledge.

N/Avs6.8%

Terminal-Bench (Agentic Coding & Terminal Use)

Autonomous multi-step tasks executed inside a real command-line terminal environment.

N/Avs32.4%

SciCode (Scientific Computing)

Research-grade coding problems drawn from real scientific and numerical workloads.

N/Avs31.8%

LiveCodeBench (Competitive Coding)

Contamination-free competitive programming problems collected continuously over time.

63%vs53.2%

AIME (Advanced Math Competition)

American Invitational Mathematics Examination — multi-step Olympiad-level reasoning.

N/Avs55.4%

Long-Context Reasoning

Accuracy when reasoning over very large inputs that fill the model context window.

N/Avs65.4%

Generation Speed (Throughput)

Tokens emitted per second during active generation streaming.

140 tok/s vs78 tok/s

Latenz & Antwortzeit

Time to first token (TTFT) and estimated end-to-end response time for a typical 500-token response.

Time to First Token (TTFT)Lower is better

Qwen3-Coder 30B-A3B Instruct0.28s

Qwen 2.5 Coder 32B0.54s

Est. End-to-End Response (500 tokens)TTFT + generation time

Qwen3-Coder 30B-A3B Instruct3.85s

Qwen 2.5 Coder 32B6.95s

Interaktiver Token-Kosten-Simulator

Adjust sliders to calculate projected monthly cloud API spending with prompt caching discounts.

Daily Input Tokens100K / day

10K500K1M

Daily Output Tokens30K / day

5K150K300K

Prompt Cache Hit Ratio50% cached

0% (Cold)50%90% (Warm)

Qwen3-Coder 30B-A3B Instruct Monthly API Bill

$0.00 / month

$0/1M in · $0/1M out

Qwen 2.5 Coder 32B Monthly API Bill

$0.90 / month

$0.2/1M in · $0.6/1M out

Qwen3-Coder 30B-A3B Instruct saves approximately $0.90 / month (100%) under this workload.1.0x Savings

Qwen3-Coder 30B-A3B Instruct · Strengths & Key Advantages

262K Kontextfenster und bis zu 8K maximale Output-Token (MMLU-Pro 74% · Arena Elo 1285)
Hohe Generierungsgeschwindigkeit von 140 Token/Sekunde bei nur 280 ms Erstlatenz (TTFT)
Offene Modellgewichte (30B total (3B MoE)) für lokales Self-Hosting und Fine-Tuning über vLLM, SGLang oder Ollama

Considerations & Trade-offs

Erfordert dedizierte GPU-Hardware mit ausreichend VRAM für den lokalen Betrieb in voller Präzision
Bei extrem komplexen mehrstufigen Forschungsfragen bieten schwere Reasoning-Spezialmodelle noch tiefere Analysen

Qwen 2.5 Coder 32B · Strengths & Key Advantages

128K Kontextfenster und bis zu 8K maximale Output-Token (SWE-bench Verified 48.6% · GPQA Diamond 48.2%)
Hohe Generierungsgeschwindigkeit von 78 Token/Sekunde bei nur 540 ms Erstlatenz (TTFT)
Offene Modellgewichte (32B) für lokales Self-Hosting und Fine-Tuning über vLLM, SGLang oder Ollama

Considerations & Trade-offs

Erfordert dedizierte GPU-Hardware mit ausreichend VRAM für den lokalen Betrieb in voller Präzision
Bei extrem komplexen mehrstufigen Forschungsfragen bieten schwere Reasoning-Spezialmodelle noch tiefere Analysen

Token Use & Task Economics

Estimated cost and token consumption to complete one representative agentic task.

Metric	Qwen3-Coder 30B-A3B Instruct	Qwen 2.5 Coder 32B
Cost per Task Estimated USD to finish one task	$0.00	$0.18
Output Tokens / Task Tokens generated per task	15K	35K
Reasoning Tokens / Task Thinking tokens burned per task	0K	0K

Technische Spezifikationen

Architecture, release timeline, capabilities, and modalities.

Feature	Qwen3-Coder 30B-A3B Instruct	Qwen 2.5 Coder 32B
Official API Model ID (SDK)	qwen3-coder-30b-a3b-instruct	qwen-2-5-coder-32b-instruct
API Protocol	OpenAI-Compatible REST / vLLM / SGLang / HuggingFace TGI	OpenAI-Compatible REST / vLLM / SGLang / HuggingFace TGI
Release Date	2025-04	2024-11
Knowledge Cutoff	March 2025	September 2024
Architecture	Compact Sparse Mixture-of-Experts Transformer	Dense Code-Specialized Transformer
Parameters	30B total (3B active MoE)	32B
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)	Flat rate ($0.2/1M in) across full 128K window
Prompt Cache Policy & TTL	Automatic prefix KV-caching in vLLM / SGLang (0 cost)	Cached input at $0.04/1M 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	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) · 19 GB VRAM (4-bit Quantized) · 65 GB VRAM (Native BF16)	BF16 / FP16, FP8 Dynamic, INT4 AWQ / GPTQ, GGUF (Q4_K_M / Q8_0) · 20 GB VRAM (4-bit Quantized) · 69 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

Qwen3-Coder 30B-A3B Instruct vs Qwen 2.5 Coder 32B: Schnelle Antworten (FAQ)

Which is more intelligent, Qwen3-Coder 30B-A3B Instruct or Qwen 2.5 Coder 32B?

Qwen3-Coder 30B-A3B Instruct is more intelligent. It scores 76 compared with 43 on the composite Intelligence Index.

Which model is faster?

Qwen3-Coder 30B-A3B Instruct is faster, generating 140 tokens per second compared with 78 tokens per second.

Which model is cheaper to run?

Qwen3-Coder 30B-A3B Instruct is cheaper at $0.00 per 1M tokens blended, compared with $0.13 (7:2:1 cache:input:output ratio).

Which model has lower latency?

Qwen3-Coder 30B-A3B Instruct has lower latency, with a time to first token of 0.28s compared with 0.54s.

Which model has a larger context window?

Qwen3-Coder 30B-A3B Instruct has a larger context window, supporting 262.1K tokens compared with 128K tokens.

Fazit

Sowohl Qwen3-Coder 30B-A3B Instruct (Optimal für Self-Hosting im eigenen Rechenzentrum, Fine-Tuning und volle Datensouveränität, Enterprise-RAG-Systeme und skalierbare API-Pipelines.) als auch Qwen 2.5 Coder 32B (Optimal für lokale Softwareentwicklung und Codegenerierung auf Basis offener Modellgewichte, Enterprise-RAG-Systeme und skalierbare API-Pipelines.) überzeugen im Produktionseinsatz: Qwen3-Coder 30B-A3B Instruct führt bei komplexem Reasoning und Coding, Qwen3-Coder 30B-A3B Instruct liefert den höheren Token-Durchsatz und Qwen3-Coder 30B-A3B Instruct ist die kosteneffizienteste Wahl für große API-Workloads.

Häufig gestellte Fragen (FAQ)

Welches Modell ist besser für Coding und Reasoning – Qwen3-Coder 30B-A3B Instruct oder Qwen 2.5 Coder 32B?+

Im Gesamt-Intelligenzindex und bei Software-Benchmarks liegt Qwen3-Coder 30B-A3B Instruct vorn. Prüfen Sie oben die SWE-bench- und GPQA-Werte für Ihren Anwendungsfall.

Wie unterscheiden sich die API-Preise von Qwen3-Coder 30B-A3B Instruct und Qwen 2.5 Coder 32B?+

Qwen3-Coder 30B-A3B Instruct kostet $0.00 / $0.00 pro 1M Input-/Output-Tokens, Qwen 2.5 Coder 32B liegt bei $0.20 / $0.60. Beim gewichteten Gesamtpreis ist Qwen3-Coder 30B-A3B Instruct günstiger.

Welches Modell hat das größere Kontextfenster und die höhere Geschwindigkeit?+

Qwen3-Coder 30B-A3B Instruct bietet 262.1K Tokens Kontext gegenüber 128K Tokens bei Qwen 2.5 Coder 32B. Bei der Generierungsgeschwindigkeit liegt Qwen3-Coder 30B-A3B Instruct vorn.

AI Terminology & Guide

Explore Glossary →

Understand key metrics like context window, token pricing, reasoning mode, and SWE-bench scores in our comprehensive AI glossary.

Tokens & Pricing Context Window AI Benchmarks Reasoning Models Tool Calling Model Context Protocol (MCP)

Qwen3-Coder 30B-A3B Instruct vs Qwen 2.5 Coder 32B

Beliebige Modelle wechseln & vergleichen

Qwen3-Coder 30B-A3B Instruct

Qwen 2.5 Coder 32B

Wer gewinnt in welcher Disziplin?

Geprüfte Benchmark-Ergebnisse im Vergleich

Latenz & Antwortzeit

Interaktiver Token-Kosten-Simulator

Qwen3-Coder 30B-A3B Instruct · Strengths & Key Advantages

Considerations & Trade-offs

Qwen 2.5 Coder 32B · Strengths & Key Advantages

Considerations & Trade-offs

Token Use & Task Economics

Technische Spezifikationen

Qwen3-Coder 30B-A3B Instruct vs Qwen 2.5 Coder 32B: Schnelle Antworten (FAQ)

Fazit

Häufig gestellte Fragen (FAQ)

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