Chapter 3
Results
Your predictions were: Kimi K3 is the brightest, the others will be on par, and multi-step questions will cause the problems.
| Model | Right | $ per 1k questions | Time | Output tokens |
|---|---|---|---|---|
| GLM 5.3 Flash | 23/23 | $0.46 | 2.3s | 163 |
| Nemotron 3.5 Lightning thinks out loud | 23/23 | $0.44 | 3.8s | 1,471 |
| GPT-OSS 120B made up a margin | 22/23 | $0.51 | 1.4s | 234 |
| MiniMax M3 | 23/23 | $0.59 | 2.4s | 205 |
| DeepSeek V4.1 Flash marker rounding glitch | 23/23 | $0.89 | 2.2s | 129 |
| GLM 5.3 | 23/23 | $2.09 | 4.4s | 249 |
| Nemotron 3 Ultra | 23/23 | $2.29 | 1.8s | 211 |
| Qwen 3.8 Max | 23/23 | $9.67 | 4.5s | 569 |
| Kimi K3 most expensive | 23/23 | $12.06 | 8.1s | 241 |
DeepSeek is shown as 23/23 because its one “miss” was a flaw in the marker (see the next chapter). Prices are Fireworks serverless standard tier, checked 14 Oct 2026.
How the predictions did
| Prediction | Verdict |
|---|---|
| Kimi K3 is the brightest | It tied for first, alongside models costing a small fraction of its price |
| The others will be on par | Right. 8 of 9 are effectively perfect |
| Multi-step questions cause the issues | Not here. All 7 hard questions were answered correctly by every model |
What to take from it
Price per token isn’t price per question
Nemotron Lightning is the cheapest per token ($0.05 in, $0.20 out per million). But it writes about 1,470 tokens of thinking per question, against about 160 for GLM Flash. You pay for that thinking, so it ends up the same price per question and slower.
Most of the cost is the schema, not the answer
Each question sends about 2,800 tokens of table descriptions and gets back about 200. The schema is identical every time, and Fireworks charges much less for input it has already seen (“cached input”, around a fifth of the price for GLM Flash). Caching that repeated prefix is the next big saving, and a good thing to bring up in a technical sales conversation.
Recommendation, for now
GLM 5.3 Flash: perfect score, second-cheapest, quick. But see the honest caveat in the next chapter.