Qwen3 235B-A22B vs Granite 3.3 8B
Benchmarks, API Token Pricing, Context Window & Speed Comparison
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Qwen3 235B-A22B by Alibaba and Granite 3.3 8B by IBM 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, both models offer identical input token rates. On output generation, costs are matched. In data-heavy architectures requiring extensive repository or document analysis, Qwen3 235B-A22B provides an expanded memory horizon with 131K tokens. In latency-sensitive production environments, Granite 3.3 8B 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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Qwen3 235B-A22B
Modèle phare MoE open sourced'Alibaba landmark 235B ouvert MoE modèle de fondation achieving de pointe performances avec 22B actif inférence efficacité.
$0.00 / 1M
$0.00 / 1M
131.1K tokens
48.5%
Granite 3.3 8B
Open source d'entreprised'IBM à poids ouverts d'entreprise modèle de fondation avec Apache 2.0 licensing, verified copyright indemnity, et strict sécurité guardrails.
$0.00 / 1M
$0.00 / 1M
128K tokens
N/A
Différences Clés en un Coup d’Œil
- Both models cost the same under this workload — choose on capability, not price.
- Qwen3 235B-A22B has a larger context window (131K vs 128K tokens).
- Granite 3.3 8B is faster at 140 tok/s vs 68 tok/s.
| Caractéristique | Qwen3 235B-A22B | Granite 3.3 8B |
|---|---|---|
Intelligence Index Composite reasoning & knowledge score (0-100) | 83 | 74 |
Provider | Alibaba | IBM |
Input Price Cost per million input tokens | $0.00 / 1M | $0.00 / 1M |
Output Price Cost per million output tokens | $0.00 / 1M | $0.00 / 1M |
Blended Price Weighted 7:2:1 cache:input:output per 1M | $0.00 / 1M | $0.00 / 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 | 16K | 8K |
Throughput Speed Tokens generated per second | 68 tok/s | 140 tok/s |
SWE-bench Verified Real-world software engineering | 48.5% | N/A |
GPQA Diamond Graduate-level reasoning | 67% | N/A |
Qui gagne où ?
At-a-glance decision frameworkEvaluations ongoing
Scores 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.
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.
Qwen3 235B-A22B · Strengths & Key Advantages
- phare intelligence avec 22B actif inférence vitesse et resource efficacité
- supérieur code architecture compréhension et dépôt-level refactorisation
- Completely à poids ouverts allowing sur-premises d'entreprise air-gapped déploiement
Considerations & Trade-offs
- nécessite clustered GPU infrastructure pour complet unquantized weights
- pensée mode produces lengthy traces qui increase mémoire cache empreinte
Granite 3.3 8B · Strengths & Key Advantages
- complet Apache 2.0 commercial licensing backed par IBM intellectual property indemnification
- Trained purely sur clean, legally vetted d'entreprise datasets
- fonctionne effortlessly sur unique grand public GPUs ou CPU server instances
Considerations & Trade-offs
- inférieur général codage et competition mathématiques ceiling que de pointe giants
- texte-seulement interface sans visuel document ingestion
Token Use & Task Economics
Estimated cost and token consumption to complete one representative agentic task.
| Metric | Qwen3 235B-A22B | Granite 3.3 8B |
|---|---|---|
Cost per Task Estimated USD to finish one task | $0.00 | $0.00 |
Output Tokens / Task Tokens generated per task | 22K | 12K |
Reasoning Tokens / Task Thinking tokens burned per task | 8K | 2K |
Spécifications Techniques
Architecture, release timeline, capabilities, and modalities.
| Caractéristique | Qwen3 235B-A22B | Granite 3.3 8B |
|---|---|---|
| Official API Model ID (SDK) | qwen3-235b-a22b-instruct | granite-3-3-8b |
| API Protocol | OpenAI-Compatible REST / vLLM / SGLang / HuggingFace TGI | OpenAI-Compatible REST / vLLM / SGLang / HuggingFace TGI |
| Release Date | 2025-04 | 2025-04 |
| Knowledge Cutoff | February 2025 | January 2025 |
| Architecture | Sparse Mixture-of-Experts Autoregressive Transformer | Enterprise-Calibrated Open Transformer |
| Parameters | 235B total (22B active MoE) | 8B |
| 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) | Free across full context window (only KV-cache VRAM scales) |
| Prompt Cache Policy & TTL | Automatic prefix KV-caching in vLLM / SGLang (0 cost) | Automatic prefix KV-caching in vLLM / SGLang (0 cost) |
| 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) · 146 GB VRAM (4-bit Quantized) · 505 GB VRAM (Native BF16) | BF16 / FP16, FP8 Dynamic, INT4 AWQ / GPTQ, GGUF (Q4_K_M / Q8_0) · 5 GB VRAM (4-bit Quantized) · 17 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 235B-A22B vs Granite 3.3 8B: Réponses Rapides (FAQ)
Which is more intelligent, Qwen3 235B-A22B or Granite 3.3 8B?
Qwen3 235B-A22B is more intelligent. It scores 83 compared with 74 on the composite Intelligence Index.
Which model is faster?
Granite 3.3 8B is faster, generating 140 tokens per second compared with 68 tokens per second.
Which model has lower latency?
Granite 3.3 8B has lower latency, with a time to first token of 0.28s compared with 0.62s.
Which model has a larger context window?
Qwen3 235B-A22B has a larger context window, supporting 131.1K tokens compared with 128K tokens.
Verdict
Verdict & Model Selection Recommendation: Choose Qwen3 235B-A22B if your priority is Enterprise self-hosted software engineering, private data analysis, and advanced research. Its performance in demanding tasks justifies its operational token cost. Choose Granite 3.3 8B if your workflow prioritizes Corporate compliance automation, internal enterprise documentation, and legal data review. For large-scale batch processing or customer-facing conversational interfaces, its balance of speed and economics delivers superior ROI.
Questions fréquentes
How does Qwen3 235B-A22B compare to Granite 3.3 8B in API token pricing and cost per token?+
Both Qwen3 235B-A22B and Granite 3.3 8B are open-weights foundation models available at $0.00 direct API cost when self-hosted, or offered at identical baseline rates across major inference providers. Model selection depends on GPU deployment sizing and specialized benchmark performance rather than raw token price.
Which model has a larger context window: Qwen3 235B-A22B or Granite 3.3 8B?+
Qwen3 235B-A22B supports a context window of 131K tokens (~98k words), while Granite 3.3 8B supports 128K tokens (~96k words). Therefore, Qwen3 235B-A22B can process more textual data and document context in a single prompt.
Which AI model is better for software engineering and coding: Qwen3 235B-A22B or Granite 3.3 8B?+
Qwen3 235B-A22B scores 48.5% on SWE-bench Verified, while Granite 3.3 8B scores competitively for software generation. For complex autonomous debugging and agentic engineering, Qwen3 235B-A22B provides robust code generation and tool calling.
What is the difference in reasoning capability and extended thinking between Qwen3 235B-A22B and Granite 3.3 8B?+
Qwen3 235B-A22B features native chain-of-thought extended thinking mode with configurable reasoning effort. In contrast, Granite 3.3 8B provides chain-of-thought reasoning depth for complex math and science.
How does inference speed (tokens per second) compare between Qwen3 235B-A22B and Granite 3.3 8B?+
Qwen3 235B-A22B generates approximately 68 tokens per second, while Granite 3.3 8B delivers 140 tokens per second. In latency-sensitive production environments, Granite 3.3 8B provides faster time-to-first-token (TTFT) and higher streaming response velocity.
Can you implement a tiered LLM routing setup combining Qwen3 235B-A22B and Granite 3.3 8B to optimize costs?+
Yes! Tiered multi-model routing can optimize for latency and accuracy: you can route high-frequency conversational turns and latency-sensitive queries to Granite 3.3 8B, and route deep architectural or complex reasoning queries to Qwen3 235B-A22B. This balanced routing strategy maximizes system throughput and answer quality.
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: GetInfoUs Independent AI Benchmarks & Official API Specs