Gemma 4 26B A4B vs GPT-6.1 Sol
Benchmarks, API Token Pricing, Context Window & Speed Comparison
Related topics (4)
Compare Gemma 4 26B A4B by Google against GPT-6.1 Sol by OpenAI across real-world coding, reasoning, token economics, and latency.
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Gemma 4 26B A4B
MoE efficaceHighly efficient ouvert MoE modèle activating seulement 4B paramètres par token pour blazing rapide local inférence.
$0.00 / 1M
$0.00 / 1M
262.1K tokens
61%
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
- Gemma 4 26B A4B is 1.0x cheaper for API usage (input + output tokens combined).
- GPT-6.1 Sol has a larger context window (1M vs 262K tokens).
- Gemma 4 26B A4B is faster at 160 tok/s vs 120 tok/s.
- GPT-6.1 Sol leads on SWE-bench coding (82.3% vs 61.0%).
| Caractéristique | Gemma 4 26B A4B | GPT-6.1 Sol |
|---|---|---|
Intelligence Index Composite reasoning & knowledge score (0-100) | 74 | 94 |
Provider | OpenAI | |
Input Price Cost per million input tokens | $0.00 / 1M | $2.00 / 1M |
Output Price Cost per million output tokens | $0.00 / 1M | $10.00 / 1M |
Blended Price Weighted 7:2:1 cache:input:output per 1M | $0.00 / 1M | $1.47 / 1M |
Context Window Maximum input + output tokens | 262.1K tokens~393 A4 pages of text | 1M tokens~1,500 A4 pages of text |
Max Output Tokens Per single request | 33K | 128K |
Throughput Speed Tokens generated per second | 160 tok/s | 120 tok/s |
SWE-bench Verified Real-world software engineering | 61% | 82.3% |
GPQA Diamond Graduate-level reasoning | 71.5% | 80.8% |
Cached Input Price Prompt caching discount per 1M tokens | $0.00 / 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.
Gemma 4 26B A4B · Strengths & Key Advantages
- 4B actif inférence compute avec 26B paramètre expressiveness
- élevé débit sur grand public workstations
- 262K fenêtre de contexte
Considerations & Trade-offs
- nécessite enough RAM à hold tous 26B weights
- Slightly moins robust sur périphérie-case logique que dense 31B
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 | Gemma 4 26B A4B | GPT-6.1 Sol |
|---|---|---|
Cost per Task Estimated USD to finish one task | $0.00 | $2.05 |
Output Tokens / Task Tokens generated per task | 8K | 62K |
Reasoning Tokens / Task Thinking tokens burned per task | 3K | 36K |
Spécifications Techniques
Architecture, release timeline, capabilities, and modalities.
| Caractéristique | Gemma 4 26B A4B | GPT-6.1 Sol |
|---|---|---|
| Official API Model ID (SDK) | models/gemma-4-26b-a4b | gpt-6-1-sol |
| API Protocol | Google Gemini API (generateContent) + OpenAI-Compatible SDK | OpenAI Responses API & Chat Completions v1 |
| Release Date | 2026-04 | 2026-09 |
| Knowledge Cutoff | January 2026 | April 2026 |
| Architecture | Sparse Mixture-of-Experts open transformer (26B total, 4B active) | Near-Astra unified reasoning model |
| Parameters | 26B (4B active MoE) | 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) | Free across full context window (only KV-cache VRAM scales) | Flat rate ($2/1M in) across full 1000K window |
| Prompt Cache Policy & TTL | Automatic prefix KV-caching in vLLM / SGLang (0 cost) | Automatic prefix caching at $0.1/1M (no extra cache-write fee) |
| Computer Use / GUI Agent | Supported (Screen coordinate grounding, browser & desktop GUI action loops) | 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 | Built-in Google Search Grounding + Managed Python Code Execution Sandbox | 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) · Vision (~258–765 tokens per image tile) | 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) · 16 GB VRAM (4-bit Quantized) · 56 GB VRAM (Native BF16) | Cloud-Hosted Proprietary (FP8 / BF16 Tensor Core Cluster) · Zero Local VRAM Required (100% Serverless Cloud API) |
| Safety & Frontier Classification | Google DeepMind Frontier Safety Framework Verified | 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, vision | text, vision |
| Output Modalities | text | text |
Gemma 4 26B A4B vs GPT-6.1 Sol: Réponses Rapides (FAQ)
Which is more intelligent, Gemma 4 26B A4B or GPT-6.1 Sol?
GPT-6.1 Sol is more intelligent. It scores 94 compared with 74 on the composite Intelligence Index.
Which model is faster?
Gemma 4 26B A4B is faster, generating 160 tokens per second compared with 120 tokens per second.
Which model is cheaper to run?
Gemma 4 26B A4B is cheaper at $0.00 per 1M tokens blended, compared with $1.47 (7:2:1 cache:input:output ratio).
Which model has lower latency?
Gemma 4 26B A4B 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 262.1K tokens.
Verdict
Choose Gemma 4 26B A4B for élevé-débit local hébergement, privé chatbots, et rapide document analyse, or choose GPT-6.1 Sol for le meilleur valeur modèle de pointe pour professionnel et agentic travail.
Questions fréquentes
Which is cheaper: Gemma 4 26B A4B or GPT-6.1 Sol?+
Gemma 4 26B A4B costs $0/1M input and $0/1M output tokens, while GPT-6.1 Sol costs $2/1M input and $10/1M output tokens.
Which model has a larger context window: Gemma 4 26B A4B or GPT-6.1 Sol?+
Gemma 4 26B A4B supports 262K 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: Google & OpenAI Official Documentation