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GPT-5-Codex vs Qwen3-Coder 480B-A35B Instruct

Benchmarks, API Token Pricing, Context Window & Speed Comparison

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AI CompareAI BenchmarkToken PricingModel CostModel SpecsSWE-bench CodingModel VersusWhich AI ModelLLM Speed & LatencyReasoning ModelsExtended ThinkingPrompt Caching ROI
GPT-5-Codex vs Qwen3-Coder 480B-A35B Instruct: Benchmarks, Pricing & Speed Comparison

GPT-5-Codex von OpenAI und Qwen3-Coder 480B-A35B Instruct von Alibaba gehören zu den führenden KI-Modellen. GPT-5-Codex bietet ein Kontextfenster von 400K Tokens bei API-Preisen von $1.25/$10.00 pro 1M Tokens, während Qwen3-Coder 480B-A35B Instruct 262.1K Tokens Kontext zu $0.00/$0.00 pro 1M Tokens bereitstellt. Hier finden Sie den vollständigen Benchmark-, Latenz- und Kostenvergleich.

Direktvergleich (VS)

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OpenAI

GPT-5-Codex

Spitzenmodell für Programmierung

GPT-5-Codex von OpenAI ist ein KI-Modell für autonomes Software-Engineering, Code-Refactoring und komplexe Fehleranalyse mit 400K Kontextfenster, 120 Token/s Durchsatz, einem Intelligenz-Index von 57/100 und $1.25/$10 pro 1M Token.

Intelligence Index57 / 100
Input Token:

$1.25 / 1M

Output Token:

$10.00 / 1M

Context Window:

400K tokens

SWE-bench Verified:

74.5%

Alibaba

Qwen3-Coder 480B-A35B Instruct

Open-Weights-Basismodell

Qwen3-Coder 480B-A35B Instruct von Alibaba ist ein KI-Modell für Self-Hosting im eigenen Rechenzentrum, Fine-Tuning und volle Datensouveränität mit 262K Kontextfenster, 52 Token/s Durchsatz, einem Intelligenz-Index von 86/100 und kostenlose

Intelligence Index86 / 100
Input Token:

$0.00 / 1M

Output Token:

$0.00 / 1M

Context Window:

262.1K tokens

SWE-bench Verified:

56.5%

Wichtigste Unterschiede auf einen Blick

  • Qwen3-Coder 480B-A35B Instruct is 1.0x cheaper for API usage (input + output tokens combined).
  • GPT-5-Codex has a larger context window (400K vs 262K tokens).
  • GPT-5-Codex is faster at 120 tok/s vs 52 tok/s.
  • GPT-5-Codex leads on SWE-bench coding (74.5% vs 56.5%).
Need definitions for token pricing, context, or benchmarks?Explore AI Glossary →
FeatureGPT-5-CodexQwen3-Coder 480B-A35B Instruct
Intelligence Index
Composite reasoning & knowledge score (0-100)
5786
Provider
OpenAIAlibaba
Input Price
Cost per million input tokens
$1.25 / 1M$0.00 / 1M
Output Price
Cost per million output tokens
$10.00 / 1M$0.00 / 1M
Blended Price
Weighted 7:2:1 cache:input:output per 1M
$1.34 / 1M$0.00 / 1M
Context Window
Maximum input + output tokens
400K tokens~600 A4 pages of text262.1K tokens~393 A4 pages of text
Max Output Tokens
Per single request
128K16K
Throughput Speed
Tokens generated per second
120 tok/s52 tok/s
SWE-bench Verified
Real-world software engineering
74.5%56.5%
GPQA Diamond
Graduate-level reasoning
80.5%70%
Cached Input Price
Prompt caching discount per 1M tokens
$0.13 / 1M$0.00 / 1M

Wer gewinnt in welcher Disziplin?

At-a-glance decision framework
Coding
GPT-5-Codex
+18.0% SWE-bench lead
Cost
Qwen3-Coder 480B-A35B Instruct
1.0x cheaper monthly runtime
Speed
GPT-5-Codex
+131% faster generation
Context
GPT-5-Codex
400K tokens window

Geprüfte Benchmark-Ergebnisse im Vergleich

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

GPT-5-CodexQwen3-Coder 480B-A35B Instruct
SWE-bench Verified (Autonomous Coding)

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

74.5%vs56.5%
GPQA Diamond (Doctorate-Level Science & Math)

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

80.5%vs70%
MMLU-Pro (Multi-task Language Understanding)

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

85.5%vs83%
LMSYS Chatbot Arena Elo

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

1360ptsvs1355pts
Humanity's Last Exam (Frontier Expert Knowledge)

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

12%vsN/A
Terminal-Bench (Agentic Coding & Terminal Use)

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

50%vsN/A
SciCode (Scientific Computing)

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

45.5%vsN/A
LiveCodeBench (Competitive Coding)

Contamination-free competitive programming problems collected continuously over time.

73.5%vs73%
AIME (Advanced Math Competition)

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

90.5%vsN/A
Long-Context Reasoning

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

77%vsN/A
Generation Speed (Throughput)

Tokens emitted per second during active generation streaming.

120 tok/s vs52 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
GPT-5-Codex1.25s
Qwen3-Coder 480B-A35B Instruct0.76s
Est. End-to-End Response (500 tokens)TTFT + generation time
GPT-5-Codex5.42s
Qwen3-Coder 480B-A35B Instruct10.38s

Interaktiver Token-Kosten-Simulator

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

100K / day
10K500K1M
30K / day
5K150K300K
50% cached
0% (Cold)50%90% (Warm)
GPT-5-Codex Monthly API Bill
$11.06 / month
$1.25/1M in · $10/1M out
Qwen3-Coder 480B-A35B Instruct Monthly API Bill
$0.00 / month
$0/1M in · $0/1M out
Qwen3-Coder 480B-A35B Instruct saves approximately $11.06 / month (100%) under this workload.1.0x Savings

GPT-5-Codex · Strengths & Key Advantages

  • 400K Kontextfenster und bis zu 128K maximale Output-Token (SWE-bench Verified 74.5% · GPQA Diamond 80.5%)
  • Hohe Generierungsgeschwindigkeit von 120 Token/Sekunde bei nur 1250 ms Erstlatenz (TTFT)
  • Effiziente API-Struktur ($1.25/$10 pro 1M Token) inklusive Prompt-Caching, Tool-Calling und strikter JSON-Ausgabe
Considerations & Trade-offs
  • Proprietäres Cloud-Modell ohne Möglichkeit zum lokalen Download der Modellgewichte
  • Bei extrem komplexen mehrstufigen Forschungsfragen bieten schwere Reasoning-Spezialmodelle noch tiefere Analysen

Qwen3-Coder 480B-A35B Instruct · Strengths & Key Advantages

  • 262K Kontextfenster und bis zu 16K maximale Output-Token (SWE-bench Verified 56.5% · GPQA Diamond 70%)
  • Hohe Generierungsgeschwindigkeit von 52 Token/Sekunde bei nur 760 ms Erstlatenz (TTFT)
  • Offene Modellgewichte (480B total (35B 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

Token Use & Task Economics

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

MetricGPT-5-CodexQwen3-Coder 480B-A35B Instruct
Cost per Task
Estimated USD to finish one task
$1.60$0.00
Output Tokens / Task
Tokens generated per task
58K25K
Reasoning Tokens / Task
Thinking tokens burned per task
32K9K

Technische Spezifikationen

Architecture, release timeline, capabilities, and modalities.

FeatureGPT-5-CodexQwen3-Coder 480B-A35B Instruct
Official API Model ID (SDK)gpt-5-codexqwen3-coder-480b-a35b-instruct
API ProtocolOpenAI Responses API & Chat Completions v1OpenAI-Compatible REST / vLLM / SGLang / HuggingFace TGI
Release Date2025-092025-04
Knowledge CutoffSeptember 2024March 2025
ArchitectureCode-specialised unified reasoning modelCode-Specialized Sparse Mixture-of-Experts Transformer
ParametersConfidential480B total (35B active MoE)
Enterprise Rate Limits (RPM / TPM)10,000 – 30,000 RPM (Tier 5 Enterprise) · 800,000 – 2,000,000 TPMUnlimited (Hardware-bound on self-hosted GPU) / 10,000 RPM Cloud · Unlimited local throughput / 10M+ TPM Managed Cloud
Long-Context Tier (>128K/200K)Flat rate ($1.25/1M in) across full 400K windowFree across full context window (only KV-cache VRAM scales)
Prompt Cache Policy & TTLAutomatic prefix caching at $0.125/1M (no extra cache-write fee)Automatic prefix KV-caching in vLLM / SGLang (0 cost)
Computer Use / GUI AgentSupported (Screen coordinate grounding, browser & desktop GUI action loops)API / CLI Tool-Use & DOM Agent ready (no raw pixel desktop click loop)
MCP & Parallel Tool CallingNative MCP Client/Server ready · Parallel function calling · 100% Strict JSON SchemaNative MCP Client/Server ready · Parallel function calling · 100% Strict JSON Schema
Web Search Grounding & SandboxNative Web Search tool + Hosted Code Interpreter & File Search RAGSupported via external Tavily/Exa Search & E2B/Docker Sandbox tools
Official Fine-Tuning SupportSupervised Fine-Tuning (SFT), Direct Preference (DPO) & Reinforcement Fine-Tuning (RFT)Full Weights SFT, LoRA, QLoRA, DPO & GRPO RL (Unsloth / Axolotl / Llama-Factory)
Multimodal Token BillingText BPE (~4 chars/token) · Vision (~258–765 tokens per image tile)Text BPE (~4 chars/token)
Quantization & Min GPU VRAMCloud-Hosted Proprietary (FP8 / BF16 Tensor Core Cluster) · Zero Local VRAM Required (100% Serverless Cloud API)BF16 / FP16, FP8 Dynamic, INT4 AWQ / GPTQ, GGUF (Q4_K_M / Q8_0) · 298 GB VRAM (4-bit Quantized) · 1032 GB VRAM (Native BF16)
Safety & Frontier ClassificationOpenAI Preparedness Framework Audited & System Card PublishedOpen-Weights System Card + Customizable Guardrail Layer (Llama Guard / ShieldGemma)
Enterprise Compliance (ZDR/HIPAA/SOC2)SOC2 Type II · GDPR · Zero Data Retention (ZDR) & HIPAA BAA Available on Enterprise Tier100% Air-Gapped On-Premises / Private VPC · Full HIPAA, GDPR & SOC2 Control
Input Modalitiestext, visiontext
Output Modalitiestexttext

GPT-5-Codex vs Qwen3-Coder 480B-A35B Instruct: Schnelle Antworten (FAQ)

Which is more intelligent, GPT-5-Codex or Qwen3-Coder 480B-A35B Instruct?

Qwen3-Coder 480B-A35B Instruct is more intelligent. It scores 86 compared with 57 on the composite Intelligence Index.

Which model is faster?

GPT-5-Codex is faster, generating 120 tokens per second compared with 52 tokens per second.

Which model is cheaper to run?

Qwen3-Coder 480B-A35B Instruct is cheaper at $0.00 per 1M tokens blended, compared with $1.34 (7:2:1 cache:input:output ratio).

Which model has lower latency?

Qwen3-Coder 480B-A35B Instruct has lower latency, with a time to first token of 0.76s compared with 1.25s.

Which model has a larger context window?

GPT-5-Codex has a larger context window, supporting 400K tokens compared with 262.1K tokens.

Fazit

Sowohl GPT-5-Codex (Optimal für autonomes Software-Engineering, Code-Refactoring und komplexe Fehleranalyse, Enterprise-RAG-Systeme und skalierbare API-Pipelines.) als auch Qwen3-Coder 480B-A35B Instruct (Optimal für Self-Hosting im eigenen Rechenzentrum, Fine-Tuning und volle Datensouveränität, Enterprise-RAG-Systeme und skalierbare API-Pipelines.) überzeugen im Produktionseinsatz: Qwen3-Coder 480B-A35B Instruct führt bei komplexem Reasoning und Coding, GPT-5-Codex liefert den höheren Token-Durchsatz und Qwen3-Coder 480B-A35B Instruct ist die kosteneffizienteste Wahl für große API-Workloads.

Häufig gestellte Fragen (FAQ)

Welches Modell ist besser für Coding und Reasoning – GPT-5-Codex oder Qwen3-Coder 480B-A35B Instruct?+

Im Gesamt-Intelligenzindex und bei Software-Benchmarks liegt Qwen3-Coder 480B-A35B Instruct vorn. Prüfen Sie oben die SWE-bench- und GPQA-Werte für Ihren Anwendungsfall.

Wie unterscheiden sich die API-Preise von GPT-5-Codex und Qwen3-Coder 480B-A35B Instruct?+

GPT-5-Codex kostet $1.25 / $10.00 pro 1M Input-/Output-Tokens, Qwen3-Coder 480B-A35B Instruct liegt bei $0.00 / $0.00. Beim gewichteten Gesamtpreis ist Qwen3-Coder 480B-A35B Instruct günstiger.

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

GPT-5-Codex bietet 400K Tokens Kontext gegenüber 262.1K Tokens bei Qwen3-Coder 480B-A35B Instruct. Bei der Generierungsgeschwindigkeit liegt GPT-5-Codex 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 & PricingContext WindowAI BenchmarksReasoning ModelsTool CallingModel Context Protocol (MCP)

Related reading

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: Offizielle API-Dokumentation von OpenAI und Alibaba, Systemkarten und unabhängige Benchmark-Auswertungen (2026).