Qwen 2.5 7B Instruct vs GPT-6.1 Sol
Benchmarks, API Token Pricing, Context Window & Speed Comparison
Related topics (4)
Compare Qwen 2.5 7B Instruct by Alibaba against GPT-6.1 Sol by OpenAI across real-world coding, reasoning, token economics, and latency.
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Live comparison: no in-depth editorial review has been published for this pair yet.
Qwen 2.5 7B Instruct
Modèle open-sourceModèle Qwen 2.5 7B Instruct développé par Alibaba, offrant une inférence rapide et une grande fiabilité pour les flux professionnels.
$0.06 / 1M
$0.12 / 1M
131.1K tokens
28.5%
GPT-6.1 Sol
Valeur proche d'Astraoffre results close à GPT-6 Astra à roughly one fifth du prix — le latest 2026 version.
$2.00 / 1M
$10.00 / 1M
1M tokens
82.3%
Différences Clés en un Coup d’Œil
- Qwen 2.5 7B Instruct is 56.3x cheaper for API usage (input + output tokens combined).
- GPT-6.1 Sol has a larger context window (1M vs 131K tokens).
- Qwen 2.5 7B Instruct is faster at 130 tok/s vs 120 tok/s.
- GPT-6.1 Sol leads on SWE-bench coding (82.3% vs 28.5%).
| Caractéristique | Qwen 2.5 7B Instruct | GPT-6.1 Sol |
|---|---|---|
Intelligence Index Composite reasoning & knowledge score (0-100) | 35 | 94 |
Provider | Alibaba | OpenAI |
Input Price Cost per million input tokens | $0.06 / 1M | $2.00 / 1M |
Output Price Cost per million output tokens | $0.12 / 1M | $10.00 / 1M |
Blended Price Weighted 7:2:1 cache:input:output per 1M | $0.03 / 1M | $1.47 / 1M |
Context Window Maximum input + output tokens | 131.1K tokens~197 A4 pages of text | 1M tokens~1,500 A4 pages of text |
Max Output Tokens Per single request | 8K | 128K |
Throughput Speed Tokens generated per second | 130 tok/s | 120 tok/s |
SWE-bench Verified Real-world software engineering | 28.5% | 82.3% |
GPQA Diamond Graduate-level reasoning | 36.8% | 80.8% |
Cached Input Price Prompt caching discount per 1M tokens | $0.01 / 1M | $0.10 / 1M |
Qui gagne où ?
At-a-glance decision frameworkScores de Benchmarks Vérifiés
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.
Latence et Temps de Réponse
Time to first token (TTFT) and estimated end-to-end response time for a typical 500-token response.
Simulateur Interactif de Coût de Tokens
Adjust sliders to calculate projected monthly cloud API spending with prompt caching discounts.
Qwen 2.5 7B Instruct · Strengths & Key Advantages
- Architecture haute performance optimisée par Alibaba
- Excellente prise en charge des sorties structurées JSON et des appels d outils
- Stabilité démontrée en environnement de production
Considerations & Trade-offs
- Nécessite une infrastructure dédiée ou des clés API adaptées pour les forts volumes
- Attention à la taille de la fenêtre de contexte pour les très grands documents
GPT-6.1 Sol · Strengths & Key Advantages
- Near-Astra results à roughly un fifth du coût
- Excellent codage et à long terme agent performances
- solide reliability avec far inférieur latence que Astra
- Near-phare results à roughly un fifth du prix
Considerations & Trade-offs
- Still marginally behind Astra sur le très hardest raisonnement
- Same prix as GPT-6 Sol, so le upgrade decision est easy
Token Use & Task Economics
Estimated cost and token consumption to complete one representative agentic task.
| Metric | Qwen 2.5 7B Instruct | GPT-6.1 Sol |
|---|---|---|
Cost per Task Estimated USD to finish one task | $0.05 | $2.05 |
Output Tokens / Task Tokens generated per task | 20K | 62K |
Reasoning Tokens / Task Thinking tokens burned per task | 0K | 36K |
Spécifications Techniques
Architecture, release timeline, capabilities, and modalities.
| Caractéristique | Qwen 2.5 7B Instruct | GPT-6.1 Sol |
|---|---|---|
| Official API Model ID (SDK) | qwen-2-5-7b-instruct | gpt-6-1-sol |
| API Protocol | OpenAI-Compatible REST / vLLM / SGLang / HuggingFace TGI | OpenAI Responses API & Chat Completions v1 |
| Release Date | 2024-09 | 2026-09 |
| Knowledge Cutoff | June 2024 | April 2026 |
| Architecture | Dense Autoregressive Transformer | Near-Astra unified reasoning model |
| Parameters | 7B | Confidential |
| Enterprise Rate Limits (RPM / TPM) | Unlimited (Hardware-bound on self-hosted GPU) / 10,000 RPM Cloud · Unlimited local throughput / 10M+ TPM Managed Cloud | 10,000 – 30,000 RPM (Tier 5 Enterprise) · 4,000,000 – 10,000,000 TPM |
| Long-Context Tier (>128K/200K) | Flat rate ($0.06/1M in) across full 131K window | Flat rate ($2/1M in) across full 1000K window |
| Prompt Cache Policy & TTL | Cached input at $0.012/1M tokens | Automatic prefix caching at $0.1/1M (no extra cache-write fee) |
| Computer Use / GUI Agent | API / CLI Tool-Use & DOM Agent ready (no raw pixel desktop click loop) | Supported (Screen coordinate grounding, browser & desktop GUI action loops) |
| 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 | Native Web Search tool + Hosted Code Interpreter & File Search RAG |
| Official Fine-Tuning Support | Full Weights SFT, LoRA, QLoRA, DPO & GRPO RL (Unsloth / Axolotl / Llama-Factory) | Supervised Fine-Tuning (SFT), Direct Preference (DPO) & Reinforcement Fine-Tuning (RFT) |
| Multimodal Token Billing | Text BPE (~4 chars/token) | Text BPE (~4 chars/token) · Vision (~258–765 tokens per image tile) |
| Quantization & Min GPU VRAM | BF16 / FP16, FP8 Dynamic, INT4 AWQ / GPTQ, GGUF (Q4_K_M / Q8_0) · 4 GB VRAM (4-bit Quantized) · 15 GB VRAM (Native BF16) | Cloud-Hosted Proprietary (FP8 / BF16 Tensor Core Cluster) · Zero Local VRAM Required (100% Serverless Cloud API) |
| Safety & Frontier Classification | Open-Weights System Card + Customizable Guardrail Layer (Llama Guard / ShieldGemma) | OpenAI Preparedness Framework Audited & System Card Published |
| Enterprise Compliance (ZDR/HIPAA/SOC2) | 100% Air-Gapped On-Premises / Private VPC · Full HIPAA, GDPR & SOC2 Control | SOC2 Type II · GDPR · Zero Data Retention (ZDR) & HIPAA BAA Available on Enterprise Tier |
| Input Modalities | text | text, vision |
| Output Modalities | text | text |
Qwen 2.5 7B Instruct vs GPT-6.1 Sol: Réponses Rapides (FAQ)
Which is more intelligent, Qwen 2.5 7B Instruct or GPT-6.1 Sol?
GPT-6.1 Sol is more intelligent. It scores 94 compared with 35 on the composite Intelligence Index.
Which model is faster?
Qwen 2.5 7B Instruct is faster, generating 130 tokens per second compared with 120 tokens per second.
Which model is cheaper to run?
Qwen 2.5 7B Instruct is cheaper at $0.03 per 1M tokens blended, compared with $1.47 (7:2:1 cache:input:output ratio).
Which model has lower latency?
Qwen 2.5 7B Instruct has lower latency, with a time to first token of 0.29s compared with 1.12s.
Which model has a larger context window?
GPT-6.1 Sol has a larger context window, supporting 1M tokens compared with 131.1K tokens.
Verdict
Choose Qwen 2.5 7B Instruct for déploiement d entreprise, automatisation et analyse de données propulsées par alibaba, or choose GPT-6.1 Sol for le meilleur valeur modèle de pointe pour professionnel et agentic travail.
Questions fréquentes
Which is cheaper: Qwen 2.5 7B Instruct or GPT-6.1 Sol?+
Qwen 2.5 7B Instruct costs $0.06/1M input and $0.12/1M output tokens, while GPT-6.1 Sol costs $2/1M input and $10/1M output tokens.
Which model has a larger context window: Qwen 2.5 7B Instruct or GPT-6.1 Sol?+
Qwen 2.5 7B Instruct supports 131K tokens whereas GPT-6.1 Sol supports 1M tokens.
Understand key metrics like context window, token pricing, reasoning mode, and SWE-bench scores in our comprehensive AI glossary.
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Les scores de benchmarks, tarifs par token et limites évoluent rapidement. Vérifiez toujours les spécifications actuelles sur la documentation officielle du fournisseur.
Sources: Alibaba & OpenAI Official Documentation