# Coding AI Benchmark 2026: 21 Models Ranked by Cost and Speed

> Coding AI benchmark 2026: we analyzed 21 frontier and open-weights models from Slopalytics ranked by coding intelligence, cost per completed task, token consumption, and execution speed. While Claude Opus 5.5 takes the top intelligence score at 51.70, it costs $13.32 per task. Meanwhile, OpenAI's GPT-6.1 Terra hits 47.61 at just $1.37 per task, delivering a 90% cost reduction for 92% of the intelligence. Selecting the wrong model in autonomous coding loops quickly drains API budgets without improving pull requests. In this deep dive, we break down why Claude Sonnet 5.5 burns 342K tokens per run, why Claude Haiku 5.5 ($0.54) and DeepSeek V4.1 Flash ($0.66) dominate sub-dollar background triage, why Grok 4.7 and Gemini 3.8 Flash are severe value traps, and how to deploy a 3-tier production routing architecture that cuts agent infrastructure costs by over 80%.

- Published: 2026-11-01
- Duration: 10:58
- Video: https://www.youtube.com/watch?v=SZ1Cz5xq6N0
- Article: https://buildverse.fun/videos/coding-ai-benchmark-2026-21-models-ranked-by-cost-and-speed
- Source: Buildverse (https://buildverse.fun)

## Chapters

- [0:00](https://www.youtube.com/watch?v=SZ1Cz5xq6N0&t=0) Coding AI Benchmark: Opus vs Terra
- [0:27](https://www.youtube.com/watch?v=SZ1Cz5xq6N0&t=27) The 4 Production Pillars
- [1:06](https://www.youtube.com/watch?v=SZ1Cz5xq6N0&t=66) Claude Opus 5.5: Peak Reasoning
- [2:18](https://www.youtube.com/watch?v=SZ1Cz5xq6N0&t=138) Claude Sonnet 5.5: The Verbosity Trap
- [3:22](https://www.youtube.com/watch?v=SZ1Cz5xq6N0&t=202) GPT-6.1 Terra and Sol: Economic Winners
- [5:14](https://www.youtube.com/watch?v=SZ1Cz5xq6N0&t=314) Haiku 5.5 and DeepSeek: Sub-Dollar Scale
- [6:40](https://www.youtube.com/watch?v=SZ1Cz5xq6N0&t=400) Mid-Tier Reasoning: Kimi, Fable, Argon
- [8:18](https://www.youtube.com/watch?v=SZ1Cz5xq6N0&t=498) Value Traps: Grok 4.7 and Gemini 3.8
- [9:15](https://www.youtube.com/watch?v=SZ1Cz5xq6N0&t=555) Latency Extremes: Muse Wonder vs MiMo
- [10:06](https://www.youtube.com/watch?v=SZ1Cz5xq6N0&t=606) The 3-Tier Production Architecture

## Commands & links

```text
Slopalytics Coding Intelligence Benchmark: https://slopalytics.com
```

## Article

### Coding AI Benchmark: Opus vs Terra

70, but it costs 13 dollars and 32 cents for every single task it runs. 61 at just 1 dollar and 37 cents per task. That is a 90% cost reduction for 92% of the raw coding intelligence.

### The 4 Production Pillars

If you are running automated coding agents, continuous integration loops, or code refactoring pipelines, selecting the wrong model will destroy your engineering budget without improving your pull requests.

To find out exactly how today's models perform in production environments, we pulled the complete coding frontier benchmark from Slopalytics, covering 21 models ranked across intelligence score, task completion cost, token consumption, and execution time.

### Claude Opus 5.5: Peak Reasoning

The findings expose massive performance gaps, surprising efficiency winners, and two multi-million parameter models that are total money pits. 5. Both models represent Anthropic's flagship coding reasoning capabilities. 70.

5 demonstrates the lowest hallucination rate and the highest task completion reliability. 5 comes with a heavy price tag. On average, a standard coding benchmark run consumes 221,177 tokens and costs 13 dollars and 32 cents. 5 a specialized weapon.

You deploy it when failure is not an option, such as critical security audits or database migration orchestrations, but not for everyday repetitive commits.

### Claude Sonnet 5.5: The Verbosity Trap

47. 5 is priced at 2 dollars per million input tokens and 10 dollars per million output tokens, which appears significantly cheaper than Opus on paper. 5 averages 11 dollars and 99 cents per benchmark task. Why does Sonnet cost almost as much as Opus despite having lower per-token rates?

The answer lies in token consumption. 5. Sonnet takes more exploratory steps, calls more read tools, and generates more conversational context before settling on the solution.

### GPT-6.1 Terra and Sol: Economic Winners

If your agent framework does not aggressively prune context, Sonnet's verbosity quickly cancels out its lower per-token pricing. Now here is the part most developers miss when architecting agent workflows: the price to performance sweet spot is no longer at the very top of the frontier. 1 generation. 1 Terra.

61 in coding intelligence, placing it within four points of the industry leader. But look at the economics: Terra averages just 1 dollar and 37 cents per coding run. It accomplishes this because it combines ultra-efficient token utilization with lower base pricing: 1 dollar per million input tokens and 5 dollars per million output tokens. 2 seconds.

5. 1 Sol, mirrors this remarkable efficiency. 5 seconds and consuming only 67,122 tokens at base rates of 2 dollars input and 10 dollars output. 5 at 13 dollars and 32 cents, the math is undeniable: you can execute nearly ten full Terra agent runs for the price of a single Opus run, with virtually indistinguishable code quality for standard full-stack feature development.

### Haiku 5.5 and DeepSeek: Sub-Dollar Scale

What happens when you need high-volume, continuous automation, like auto-reviewing every pull request, running lint fixers, or writing unit tests across hundreds of microservices? This is where the sub-dollar budget champions dominate. 5 is currently the undisputed king of budget coding intelligence. 09 in coding intelligence while costing a staggering 54 cents per task.

That is 54 cents for an autonomous coding run that reads files, edits functions, and verifies test suites. 5 outperforms many previous generation flagship models while costing less than a cup of coffee for a full day of background work. 1 Flash. 2 seconds per task.

### Mid-Tier Reasoning: Kimi, Fable, Argon

1 Flash provides the most cost-effective open-weights-accessible API for developer platforms building multi-agent harnesses. Now let us examine the mid-tier specialists. 14 in coding intelligence at 3 dollars and 53 cents per task with an average of 71,781 tokens. Kimi proves that specialized reasoning models from emerging labs can punch far above their weight class, providing a competitive alternative to proprietary American frontier APIs.

1. 78, which is solid, but it costs a staggering 17 dollars and 46 cents per task. 1 the most expensive Anthropic model in the entire benchmark. Why?

Because Fable carries a base pricing of 10 dollars per million input tokens and 50 dollars per million output tokens. Unless your workflow specifically requires Fable's unique reasoning quirks, running it for routine coding tasks is financially reckless.

### Value Traps: Grok 4.7 and Gemini 3.8

Then we have Gemini 4 Argon. 25 in coding intelligence at 6 dollars and 31 cents per task. 6 seconds per task, which is over 31 minutes. In an interactive pair-programming harness, waiting half an hour for an agent turn breaks developer flow.

8 Flash. 1 Flash. 7 costs 13 dollars and 27 cents per task. You are paying frontier Opus prices for sub-budget intelligence.

2 seconds.

### Latency Extremes: Muse Wonder vs MiMo

Using either of these two models in autonomous coding loops will bleed your API budget with almost nothing to show for it. Finally, look at the speed and latency extremes. 53 at 7 dollars and 91 cents. 6 seconds, which is over 36 minutes.

MiMo is built for overnight batch runs where wall-clock time does not matter, whereas Muse Wonder is optimized for rapid feedback. To maximize both code quality and capital efficiency in 2026, the optimal strategy is a three-tier routing architecture.

### The 3-Tier Production Architecture

1 Flash at under 70 cents. 1 Terra at 1 dollar and 37 cents. 5 at 13 dollars and 32 cents exclusively for high-stakes architecture reviews and root-cause debugging. This tiered setup cuts your overall agent infrastructure costs by over 80% while keeping your codebase at the absolute frontier of code intelligence.

Check out the complete benchmark breakdown on the channel, and we will see you in the next build.