Four providers moved in the same three weeks
If you're picking an AI model API for a hackathon project this month, the landscape you'd be comparing against has almost entirely turned over since late June. OpenAI's GPT-5.6 family reached general availability on July 9. Meta shipped Muse Spark 1.1 the same day, its first model it charges for through its own API. Anthropic's Claude Sonnet 5 has been out since June 30, with introductory pricing that steps up at the end of August. Grok 4.5 is live on the xAI API. Any comparison written before July 9 is missing a third of this list.
None of that changes the actual question a team has to answer in the first hour of a hackathon: which one do we build on. Benchmark leaderboards don't answer that well, because they're not measuring what you're optimizing for over a 24 to 48 hour build.
The options, as of mid-July 2026
| Model | Provider | Price (input / output per 1M tokens) | Context window | Shipped |
|---|---|---|---|---|
| GPT-5.6 (Sol) | OpenAI | $5 / $30 | Not yet published in full | July 9, 2026 |
| Claude Sonnet 5 | Anthropic | $2 / $10 through Aug 31, then $3 / $15 | 1M tokens, 128K max output | June 30, 2026 |
| Muse Spark 1.1 | Meta | $1.25 / $4.25 | Not yet published in full | July 9, 2026 |
| Grok 4.5 | xAI | $2 / $6 | Not yet published in full | Live now |
Sol is OpenAI's flagship in the 5.6 family; there are two smaller siblings (Terra and Luna) priced lower if raw capability matters less than cost for your use case. Check each provider's own pricing page before you commit, these numbers move.
What actually matters over a weekend, not a benchmark chart
Cost against a fixed budget. A team working off a $20 to $50 credit grant cares about dollars per million tokens a lot more than a percentage point on a coding benchmark. At the prices above, a chatty debugging session with a 100K-token context can burn a meaningful chunk of a small budget on the pricier end of this table and barely register on the cheaper end.
Tool-calling reliability. Most AI-track hackathon projects aren't a chat window, they're a model wired into a loop: call a search API, run some code, hit your own backend, repeat. A model that occasionally hallucinates a tool call or malforms an argument costs you debugging hours you don't have on a deadline. This isn't something a leaderboard score tells you; it's something you find out by actually wiring up your specific tools and watching what happens.
Latency you can watch happen. A demo where a judge is staring at a spinner for four seconds loses more points than a demo built on a slightly weaker model that streams back instantly. If your project's centerpiece moment is a model response, test that response time under the same load you'll have on stage, not in an isolated API call at 2am with nobody else hitting the endpoint.
How fast you get from API key to first working call. Whichever provider's docs and SDK let you go from signup to a successful response with the least friction hands you back real hours on day one. This varies more than people expect, and it's worth ten minutes of testing before you commit a whole team to a stack.
Let the sponsor track decide it, if there is one
If the hackathon's AI track is sponsored by a specific provider, that usually settles the question regardless of anything above, because prize eligibility is tied to using their API. Check the rules before you optimize for cost or latency on a model that doesn't even qualify.
If it's an open track, match the model to what the project actually needs: an agentic, tool-heavy build wants reliable tool-calling more than raw output quality; a high-volume, cost-sensitive feature wants the cheapest per-token price that's still good enough; a coding-assistant-style feature wants whichever model is fastest to iterate against locally.
This table won't stay accurate
Sonnet 5's intro pricing ends August 31, after which it jumps to $3 / $10. Given four providers moved in three weeks, there's a reasonable chance another one ships or reprices before you finish reading competitor posts on this topic. Check each provider's live pricing page before you budget against anything above, and treat this table as a snapshot of mid-July 2026, not a permanent reference.
Once you've settled on a model, the harder part is usually picking a project that actually fits it and the hours you have left. hackspot takes your stack, including whichever model you just picked, and your time budget, and returns a shortlist of scoped ideas instead of a blank page. And once you've picked one, the hackspot CLI scaffolds it straight into a project and hands it to your coding agent.