The strongest AI model on a leaderboard is not automatically the right model for a product, research program or engineering team. Cost, latency, deployment control and input formats can matter as much as raw reasoning performance.
A comparison reported by First Page Sage Blog evaluated 42 large language models and ranked 15 of them using benchmark, pricing and technical data available in June 2026. Its findings offer a useful starting point, provided buyers treat the ranking as a decision aid rather than a universal purchasing order.
How the source built its model ranking
The source weighted eight factors: the Artificial Analysis Intelligence Index at 25%, SWE-bench Verified at 20%, GPQA Diamond at 15%, and context window, output speed and blended API cost at 10% each. Supported modalities and open-weight availability each accounted for the remaining 5%.
Those measures address different questions. SWE-bench Verified tests the resolution of real GitHub issues in a standardized environment, while GPQA Diamond focuses on graduate-level science questions. Context size indicates how much material a model can accept in one call; it does not, by itself, prove that the model will use every part of a long prompt effectively. Speed affects interactive experiences, and open weights can support self-hosting or fine-tuning without dependence on a single API vendor.
When public data was missing, the source applied a conservative below-average score. That choice makes a complete ranking possible, but it can also push models with incomplete reporting below models with more extensive published results.
Key takeaways
- Claude Fable 5 led the composite ranking. First Page Sage reported an Intelligence Index score of 60, 95.0% on its standardized SWE-bench source and a blended price of $7.70 per million tokens.
- GLM-5.2 stood out among open-weight choices. It was reported at 82.8% on SWE-bench Verified, with a $0.90 blended cost and an MIT license.
- Qwen 3.7 Max was the speed leader. Its reported output rate of 198 tokens per second makes it especially relevant to interactive products.
- DeepSeek V4 Flash had the lowest estimated blended price. The source listed it at about $0.15 per million tokens, while noting that its Intelligence Index score was unavailable.
- No single benchmark settles the decision. Capability, latency, price, modalities, context and deployment requirements need to be considered together.
Match the model to the workload
The most useful way to read the reported results is by operating constraint. A team paying for failed reasoning has different priorities from one serving millions of short customer interactions.
| Primary need | Model highlighted by the source | Reported reason to consider it |
|---|---|---|
| Maximum overall capability | Claude Fable 5 | Highest composite and standardized coding scores in the dataset |
| Long-running software agents | Claude Opus 4.8 | Strong coding and command-line results at a lower price than Fable 5 |
| One multimodal platform | GPT-5.5 | Text, vision, audio and image generation in one model |
| Low-cost open-weight coding | GLM-5.2 | Strong reported SWE-bench performance, MIT licensing and a $0.90 blended price |
| High-speed user interfaces | Qwen 3.7 Max | Fastest confirmed output rate in the comparison |
| Scientific and multimodal research | Gemini 3.1 Pro | 94.1% reported GPQA Diamond performance and support for text, vision, audio and video |
| Lowest API cost | DeepSeek V4 Flash | Lowest estimated blended price in the dataset |
| Self-hosted multimodal deployment | Llama 4 Maverick | Open weights and compatibility with major inference frameworks |
Where benchmark comparisons need caution
The source explicitly warned that SWE-bench Verified results above roughly 80% should be interpreted carefully because of debate about saturation and practical utility. It also noted that standardized harness results may differ from developer-published figures produced with proprietary tools.
Several entries carry additional uncertainty. MiniMax-M3’s 80.5% SWE-bench result was flagged for possible training-data contamination. Grok 4’s Intelligence Index was estimated rather than officially confirmed, while Llama 4 Maverick lacked published SWE-bench Verified and GPQA Diamond figures in the materials reviewed. GPT-5.3 Codex also lacked a standardized SWE-bench Verified result, and the listed Intelligence Index figure was preliminary.
Pricing deserves similar scrutiny. A blended figure depends on the assumed balance of input and output tokens, while self-hosting introduces infrastructure and operational costs that an API price does not capture. Latency can also vary by provider even when the underlying model is the same.
A practical way to make the final choice
- Define the task and the cost of an incorrect result.
- Eliminate models that fail hard requirements such as data residency, modalities, context capacity or licensing.
- Shortlist options using benchmark results that resemble the actual workload.
- Run the same representative test set against every shortlisted model.
- Measure quality, latency and total cost together, including retries and human review.
Model rankings will continue to move, but a repeatable evaluation process is more durable than any leaderboard position. The best deployment is the one that meets a clearly defined quality threshold at an acceptable operational cost.
Inspired by this post on First Page Sage Blog.
