{"id":111917,"date":"2025-02-05T09:46:51","date_gmt":"2025-02-05T17:46:51","guid":{"rendered":"https:\/\/www.backblaze.com\/blog\/?p=111917"},"modified":"2026-07-31T13:47:24","modified_gmt":"2026-07-31T20:47:24","slug":"ai-reasoning-models","status":"publish","type":"post","link":"https:\/\/www.backblaze.com\/blog\/ai-reasoning-models\/","title":{"rendered":"AI Reasoning Models: A Guide to the Top Models"},"content":{"rendered":"\r\n
\"A<\/figure>\r\n\r\n\r\n\r\n\r\n\r\n

 <\/p>\r\n

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.<\/em><\/p>\r\n

If you haven\u2019t been able to keep pace with the AI news cycle, you\u2019d be forgiven. I work at a tech company, and it\u2019s 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\u2019s Deep Think mode. And DeepSeek, xAI and Anthropic, among others, are all competing on the same benchmarks.<\/span><\/p>\r\n\r\n\r\n\r\n

In the spirit of our AI 101 series<\/a>, I\u2019ll do my level best to recap the finer points and decode some of the more esoteric terms you\u2019re likely to encounter (Like: WTH is a \u201cmixture of experts\u201d? That sounds like a party I want to be invited to, but will definitely skip at the last minute.)<\/p>\r\n\r\n\r\n\r\n

AI reasoning models compared<\/h2>\r\n\n
\n\n\n\n\t\n\n\t\n\t\n\t\n\t\n\t\n\t
Model<\/div><\/th>
Best For<\/div><\/th>
Context Window<\/div><\/th>
Input\/Output Price (per 1M tokens)<\/div><\/th>
Open Weight<\/div><\/th>
Artificial Analysis Intelligence Index<\/div><\/th>\n<\/tr>\n<\/thead>\n
GPT-5.5<\/div><\/td>
Most complex professional work<\/div><\/td>
1M<\/div><\/td>
$5\/$30<\/div><\/td>
No<\/div><\/td>
60.2<\/div><\/td>\n<\/tr>\n
DeepSeek-V4<\/div><\/td>
Cost-sensitive or self-hosted applications<\/div><\/td>
1M<\/div><\/td>
$0.14\u2013$0.30 (varies by provider)\/$0.28\u2013$2.19<\/div><\/td>
Yes (MIT)<\/div><\/td>
51.5<\/div><\/td>\n<\/tr>\n
Gemini 3.5<\/div><\/td>
Multimodal understanding<\/div><\/td>
1M<\/div><\/td>
$1.50\/$9<\/div><\/td>
No<\/div><\/td>
55.3<\/div><\/td>\n<\/tr>\n
Claude Opus 4.8<\/div><\/td>
Agentic coding, long-horizon task execution<\/div><\/td>
1M<\/div><\/td>
$5\/$25<\/div><\/td>
No<\/div><\/td>
61.4<\/div><\/td>\n<\/tr>\n
Grok 4.3<\/div><\/td>
Real-time information, research with live data access<\/div><\/td>
1M<\/div><\/td>
$1.25\/$2.50<\/div><\/td>
No<\/div><\/td>
53.2<\/div><\/td>\n<\/tr>\n
*The Artificial Analysis Intelligence Index<\/a> 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.<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<\/div>\n\r\n

\u00a0<\/h2>\r\n

The latest AI reasoning models<\/h2>\r\n\r\n\r\n\r\n

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.\u00a0<\/p>\r\n\r\n\r\n\r\n

Here are the reasoning models<\/span> that you should know about.<\/p>\r\n\r\n\r\n\r\n

1. GPT-5.5<\/span><\/h3>\r\n

Developer: <\/b>OpenAI<\/span><\/p>\r\n

Released:<\/b> April 23, 2026\u00a0<\/span><\/p>\r\n

Best for:<\/b> Coding use cases, tool-heavy agents, grounded assistants, long-context retrieval, product-spec-to-plan workflows, and customer-facing workflows<\/span><\/p>\r\n

License:<\/b> Proprietary<\/span><\/p>\r\n

Pricing:<\/b> $5\/M input, $30\/M output\u00a0<\/span><\/p>\r\n

Context window:<\/b> 1M tokens<\/span><\/p>\r\n

GPT-5.5<\/span><\/a> is OpenAI\u2019s 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. <\/span>GPT-5.5 Instant<\/span><\/a> is OpenAI’s free version and is the updated default ChatGPT model.<\/span><\/p>\r\n

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.\u00a0<\/span><\/p>\r\n\r\n\r\n\r\n

2. DeepSeek-V4 <\/span><\/h3>\r\n

Developer: <\/b>DeepSeek<\/span><\/p>\r\n

Released:<\/b> April 24, 2026<\/span><\/p>\r\n

Best for:<\/b> Math, logical reasoning, code, cost-sensitive, or self-hosted applications\u00a0<\/span><\/p>\r\n

License:<\/b> MIT (open weight)<\/span><\/p>\r\n

Pricing:<\/b> $0.14\/M\u2013$0.30\/M input, $0.28\u2013$0.50\/M\u00a0<\/span><\/p>\r\n

Context window:<\/b> 1M tokens\u00a0<\/span><\/p>\r\n\r\n\r\n\r\n

 <\/p>\r\n

Unless you\u2019ve been under a rock, you\u2019ve heard about this one. DeepSeek rattled the AI industry and financial markets with its release of R1<\/a>, challenging OpenAI\u2019s models on performance, pricing, and open-source availability. (We love a good open-source release<\/a>.)\u00a0<\/p>\r\n

The newest flagship model, <\/span>DeepSeek-V4-Pro<\/span><\/a>, 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.<\/span><\/p>\r\n

DeepSeek\u2019s efficiency claims could have far-reaching impacts for enterprises looking to build AI at a fraction of the cost. <\/span>It’s MIT-licensed and self-hostable, making it an option for teams that want frontier-level performance without API dependencies.<\/span><\/p>\r\n\r\n\r\n\r\n

3. Gemini 3.5 Flash<\/span><\/h3>\r\n\r\n\r\n\r\n

 <\/p>\r\n

Developer: <\/b>Google<\/span><\/p>\r\n

Released:<\/b> May 19, 2026\u00a0<\/span><\/p>\r\n

Best for:<\/b> Executing complex, agentic workflows, as well as long context and multimodal understanding\u00a0<\/span><\/p>\r\n

License:<\/b> Proprietary<\/span><\/p>\r\n

Pricing:<\/b> $1.50\/M input, $9\/M output\u00a0<\/span><\/p>\r\n

Context window:<\/b> 1M tokens<\/span><\/p>\r\n

Google is positioning <\/span>Gemini 3.5 Flash<\/span><\/a> as an agentic and coding model. <\/span>It’s built to run multi-step agentic workflows, especially when paired with Google’s Antigravity harness for deploying collaborative subagents.<\/span><\/p>\r\n

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.<\/span><\/p>\r\n

4. Claude Opus 4.8<\/span><\/h3>\r\n

Developer: <\/b>Anthropic<\/span><\/p>\r\n

Released:<\/b> May 28, 2026<\/span><\/p>\r\n

Best for:<\/b> Agentic coding, long-horizon task execution, instruction-following, complex analysis\u00a0<\/span><\/p>\r\n

License:<\/b> Proprietary<\/span><\/p>\r\n

Pricing:<\/b> $5\/M input, $25\/M output<\/span><\/p>\r\n

Context window:<\/b> 1M tokens<\/span><\/p>\r\n

Claude Opus 4.8<\/span><\/a> 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.<\/span><\/p>\r\n

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.\u00a0<\/span><\/p>\r\n

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.<\/span><\/p>\r\n

5. Grok 4.3<\/span><\/h3>\r\n

Developer: <\/b>xAI<\/span><\/p>\r\n

Released:<\/b> July 9, 2026<\/span><\/p>\r\n

Best for:<\/b> STEM reasoning, real-time information, research with live data access\u00a0<\/span><\/p>\r\n

License:<\/b> Proprietary<\/span><\/p>\r\n

Pricing:<\/b> $1.25\/M input, $2.50\/M output\u00a0<\/span><\/p>\r\n

Context window:<\/b> 1M\u00a0<\/span><\/p>\r\n

Grok 4.3<\/span><\/a> 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).<\/span><\/p>\r\n

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.<\/span><\/p>\r\n\r\n\r\n\r\n

What is an AI reasoning model anyway?<\/h2>\r\n\r\n\r\n\r\n

 <\/p>\r\n

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. <\/span><\/p>\r\n

You might see reasoning described as \u201cthinking\u201d before it delivers an answer, but do not be fooled. AI cannot yet \u201cthink\u201d or, to be fair, \u201creason\u201d in the ways that we apply those terms to humans.<\/p>\r\n

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.<\/p>\r\n\r\n\r\n\r\n