Disclaimer: Specs, pricing, and benchmarks reflect publicly available data as of mid-2026. The AI model landscape evolves rapidly; it’s recommended to verify current figures at each provider’s documentation before making infrastructure decisions.
If you haven’t been able to keep pace with the AI news cycle, you’d be forgiven. I work at a tech company, and it’s felt like bailing water with a teacup over the past few months. But the term that keeps rising to the top of the flotsam in the boat is this: reasoning models. OpenAI has its frontier models. Google has Gemini’s Deep Think mode. And DeepSeek, xAI and Anthropic, among others, are all competing on the same benchmarks.
In the spirit of our AI 101 series, I’ll do my level best to recap the finer points and decode some of the more esoteric terms you’re likely to encounter (Like: WTH is a “mixture of experts”? That sounds like a party I want to be invited to, but will definitely skip at the last minute.)
AI reasoning models compared
Model | Best For | Context Window | Input/Output Price (per 1M tokens) | Open Weight | Artificial Analysis Intelligence Index |
|---|---|---|---|---|---|
GPT-5.5 | Most complex professional work | 1M | $5/$30 | No | 60.2 |
DeepSeek-V4 | Cost-sensitive or self-hosted applications | 1M | $0.14–$0.30 (varies by provider)/$0.28–$2.19 | Yes (MIT) | 51.5 |
Gemini 3.5 | Multimodal understanding | 1M | $1.50/$9 | No | 55.3 |
Claude Opus 4.8 | Agentic coding, long-horizon task execution | 1M | $5/$25 | No | 61.4 |
Grok 4.3 | Real-time information, research with live data access | 1M | $1.25/$2.50 | No | 53.2 |
| *The Artificial Analysis Intelligence Index is a composite benchmark that aggregates scores across nine evaluations, covering agentic tasks, coding, scientific reasoning, knowledge, and long-context reasoning, to rank AI models by overall capability. | |||||
The latest AI reasoning models
The last few months have seen a flurry of activity in the AI space, with reasoning models taking center stage. The TL/DR is that reasoning models are LLMs that can self-correct before delivering a response to a prompt, though their turn time is a little longer than your standard LLM.
Here are the reasoning models that you should know about.
1. GPT-5.5
Developer: OpenAI
Released: April 23, 2026
Best for: Coding use cases, tool-heavy agents, grounded assistants, long-context retrieval, product-spec-to-plan workflows, and customer-facing workflows
License: Proprietary
Pricing: $5/M input, $30/M output
Context window: 1M tokens
GPT-5.5 is OpenAI’s newest frontier model, which the company says can handle most complex professional work. It now defaults to “medium” effort, the recommended starting point for balancing quality, latency, and cost. GPT-5.5 Instant is OpenAI’s free version and is the updated default ChatGPT model.
GPT-5.5 is pretty no-nonsense by design. It gets to the point, which is great for production workflows, but you’ll need to explicitly prompt in warmth and personality if you want it to feel more human for client services.
2. DeepSeek-V4
Developer: DeepSeek
Released: April 24, 2026
Best for: Math, logical reasoning, code, cost-sensitive, or self-hosted applications
License: MIT (open weight)
Pricing: $0.14/M–$0.30/M input, $0.28–$0.50/M
Context window: 1M tokens
Unless you’ve been under a rock, you’ve heard about this one. DeepSeek rattled the AI industry and financial markets with its release of R1, challenging OpenAI’s models on performance, pricing, and open-source availability. (We love a good open-source release.)
The newest flagship model, DeepSeek-V4-Pro, is a Mixture-of-Experts architecture with 1.6T total parameters but only 49B activated at inference time, meaning you get massive model capacity without paying the full compute cost. Like GPT-5.5, DeepSeek-V4 has tiered reasoning modes: Non-Think for fast responses, Think High for deliberate reasoning, and Think Max for pushing the model to its limits.
DeepSeek’s efficiency claims could have far-reaching impacts for enterprises looking to build AI at a fraction of the cost. It’s MIT-licensed and self-hostable, making it an option for teams that want frontier-level performance without API dependencies.
3. Gemini 3.5 Flash
Developer: Google
Released: May 19, 2026
Best for: Executing complex, agentic workflows, as well as long context and multimodal understanding
License: Proprietary
Pricing: $1.50/M input, $9/M output
Context window: 1M tokens
Google is positioning Gemini 3.5 Flash as an agentic and coding model. It’s built to run multi-step agentic workflows, especially when paired with Google’s Antigravity harness for deploying collaborative subagents.
The headline speed claim is notable, too: Google says it’s 4x faster at producing tokens per second than other frontier models. Google claims it can complete tasks that used to take developers days or auditors weeks in a fraction of the time, often at less than half the cost of other frontier models. Supported inputs include text, images, videos, audio, and PDFs.
4. Claude Opus 4.8
Developer: Anthropic
Released: May 28, 2026
Best for: Agentic coding, long-horizon task execution, instruction-following, complex analysis
License: Proprietary
Pricing: $5/M input, $25/M output
Context window: 1M tokens
Claude Opus 4.8 is an upgrade to Opus 4.7, with improvements across benchmarks and a focus on being a more effective collaborator. Fast mode now runs at 2.5x speed and is three times cheaper than on previous models, making higher-performance workflows more accessible.
According to Anthropic, one standout improvement is honesty. Opus 4.8 is more likely to flag uncertainty in its own work and less likely to confidently claim progress it hasn’t actually made. It’s about 4 times less likely than Opus 4.7 to let flaws in its own code go unnoticed.
On the agentic side, the new Dynamic Workflows feature lets Opus 4.8 plan a task, spin up hundreds of parallel subagents, and verify outputs before reporting back, enabling tasks such as full codebase migrations. The model defaults to high effort, with “extra” and “max” modes available when you need to push harder on complex or long-running tasks.
5. Grok 4.3
Developer: xAI
Released: July 9, 2026
Best for: STEM reasoning, real-time information, research with live data access
License: Proprietary
Pricing: $1.25/M input, $2.50/M output
Context window: 1M
Grok 4.3 is xAI’s current flagship, positioned as their most advanced model with a focus on low hallucination rates, agentic tool calling, and instruction following. It handles both text and image inputs and a configurable reasoning effort (none, low, medium, high).
On the capabilities side, it supports function calling, structured outputs, and it aliases a long list of older Grok 3 and Grok 4 model strings, so existing integrations pointing to those IDs will automatically route to 4.3.
What is an AI reasoning model anyway?
A reasoning model is an AI model trained specifically to work through problems step by step before producing a final answer. Without it, the LLM might generate an entire response in a single output.
You might see reasoning described as “thinking” before it delivers an answer, but do not be fooled. AI cannot yet “think” or, to be fair, “reason” in the ways that we apply those terms to humans.
Reasoning models leverage chain-of-thought prompting to guide decision-making, incorporating self-improvement mechanisms and using test-time thinking to make real-time adjustments.
- Chain-of-thought (CoT) prompting: Models break problems into logical steps (e.g., solving math problems via intermediate equations)
- Self-improvement mechanisms: Techniques like the Self-Taught Reasoner (STaR) enable iterative refinement of reasoning through automated feedback loops.
- Test-time thinking: Models can make decisions during deployment based on real-time inputs, rather than relying solely on pre-trained models or fixed strategies.
- Thinking budgets: Some models let developers control how much reasoning compute is allowed per request. Larger budgets are slower, more thorough, and more expensive.
Here are a few more terms you might come across for good measure:
- Inference compute: The computational power needed to run a reasoning model and generate predictions or outputs based on new data after the model has been trained.
- Mixture of experts approach: Using multiple specialized models (“experts”) that handle different tasks, and applying a gating mechanism to select the most relevant expert to use to make predictions based on the input data. Of note: DeepSeek used this approach to create efficiencies.
- Distillation: Using inputs and outputs from one model to train another model. Of note: OpenAI alleges this is how DeepSeek “stole” its IP.
This is all pretty cool, if linguistically painful, stuff, and it means that reasoning models are shifting perceptions of model capabilities. But they’re not without persistent challenges. Like other LLMs, they still struggle with complex reasoning failures, lack of training transparency, and cognitive biases.
Why should developers care?
Understanding the mechanics of AI reasoning models helps you make better decisions about when and how to use these models.
If the past two months (and, really, the past two years) are any indication, AI innovation will continue its blistering pace. Reasoning models, and LLMs in general, will become diverse and specialized for narrower tasks as the core technology is increasingly commoditized and cheapened. And, it’s worth noting that this is a totally normal—and expected—lifecycle when it comes to new technology.
What does it all mean for enterprises looking to build AI into their operations? Two key takeaways:
- Don’t overcommit on any one toolset or investment: Stay ahead of the changing landscape and new models—stay nimble, and keep experimenting.
- Take care of your data: What makes these models valuable for your company isn’t so much their capabilities, but your data. You need to retain it in storage that’s reliable, easy to access, and doesn’t lock you out of AI experimentation with exorbitant egress fees.
Even as AI models get better, having those fundamentals in place can only help your business and set you up to better leverage AI when it’s right for your operations.
Backblaze B2 Cloud Storage and B2 Overdrive are built for exactly this workload: always-hot, S3-compatible, no egress fees, and priced at a fraction of what hyperscalers charge.
Frequently Asked Questions
An AI reasoning model is a large language model that performs intermediate reasoning steps before generating a final answer. Rather than responding immediately, it evaluates multiple possible approaches to improve accuracy on complex tasks such as mathematics, coding, and logical problem-solving.
AI reasoning models generate a hidden reasoning trace before producing a final response. During training, reinforcement learning on verifiable outcomes—such as correct math solutions or functional code—helps the model learn reasoning strategies that produce accurate results instead of simply generating plausible-sounding answers.
A reasoning trace is the series of intermediate steps an AI model generates internally while solving a problem. These steps help the model evaluate different approaches before producing its final answer. In most reasoning models, the reasoning trace remains hidden from users.
Reasoning models are best suited for tasks that require multiple steps, logical analysis, coding, mathematics, research, and complex decision-making. For simpler requests such as summarization or straightforward writing, a standard language model is often faster and more cost-effective.
For many complex tasks, AI reasoning models can achieve higher accuracy because they spend additional computation evaluating possible solutions before responding. However, their performance still depends on the quality of the prompt and the specific model being used.
