Open Weights vs the Frontier: The Gap Is 3 Points and $8 a Million
Kimi K3 scores 60 on the Artificial Analysis Intelligence Index. Claude Opus 5 scores 63. That's the whole gap now — and the open model costs a third as much.
- Artificial Analysis has Kimi K3 (max) at 60 on its Intelligence Index. Claude Opus 5 tops the same board at 63, Fable 5 at 62. Three points is the whole frontier premium.
- On Moonshot's own model card, K3 beats Fable 5 on GPQA Diamond (93.5 vs 92.6), Terminal-Bench 2.1 (88.3 vs 88.0), and BrowseComp (91.2 vs 88.0).
- Where closed still wins: DeepSWE. GPT-5.6 Sol 73.0, Fable 5 70.0, K3 67.5. Long-horizon agentic coding is the last honest moat.
- Price: K3 is $3.00 in / $15.00 out per million tokens. Fable 5 is $10 in / $50 out. Same tier of answer, one-third the invoice.
As of August 2026 the best open-weight model, Moonshot's Kimi K3, scores 60 on the Artificial Analysis Intelligence Index against 63 for Claude Opus 5 and 62 for Claude Fable 5. Open weights now trail the closed frontier by roughly three points while costing about one-third as much per token.
Three points.
That’s the distance between the best model you can download for free and the best model money can rent. Artificial Analysis has Kimi K3 at 60 on its Intelligence Index. Claude Opus 5 leads the same board at 63, with Fable 5 at 62.
I’ve been running both benches since K3’s weights dropped July 27th. The number that matters isn’t the three points. It’s what the three points cost.
The head-to-head
Moonshot published a direct comparison table in the K3 model card. Vendor-run numbers, so read them like a spec sheet and not gospel — but they’re specific, reproducible, and they put K3 next to the closed flagships instead of next to other open models.
| Benchmark | Kimi K3 (open) | Claude Fable 5 | GPT-5.6 Sol |
|---|---|---|---|
| GPQA Diamond | 93.5 | 92.6 | 94.1 |
| Terminal-Bench 2.1 | 88.3 | 88.0 | 88.8 |
| DeepSWE | 67.5 | 70.0 | 73.0 |
| BrowseComp | 91.2 | 88.0 | 90.4 |
| AA Intelligence Index | 60 | 62 | — |
| Price /1M tokens (in / out) | $3.00 / $15.00 | $10 / $50 | — |
Fable 5’s pricing is straight from Anthropic’s own model docs: $10 per million in, $50 per million out, 1M context, 128k output.
Peep the shape of that table. On graduate-level science K3 is ahead of Fable by 0.9 and behind Sol by 0.6. On web research it beats both. That’s not a fast-follower. That’s the same tier.
Where the frontier still earns the invoice
DeepSWE. 73.0, 70.0, 67.5.
Five and a half points on long-horizon agentic coding is the whole remaining moat, and it’s the exact workload people actually pay frontier rates for. An agent that runs 40 tool calls without losing the plot is a different product than a model that answers a hard chemistry question. Everybody’s GPQA is 93. Nobody’s DeepSWE is.
The other thing closed buys you is the scaffolding. Effort sliders, task budgets, server-side fallback on refusal, prompt caching that actually gets credited back. With open weights you build that yourself or you rent it from an inference provider — and provider quality varies enough that OpenRouter shows K3 GPQA Diamond at 92.7 on one host and 90.2 on another. Same weights. Different serving stack. That variance is a real operational cost nobody puts on a leaderboard.
What open weights buy that no benchmark measures
You own the artifact.
I wrote about this after Fable 5 came back from its 19-day export ban, and the lesson didn’t expire: a closed model can be quota’d, deprecated, classifier’d, or shut off by a cabinet secretary’s letter. A file on your disk can’t. Every builder who kept an open model warm during that blackout kept shipping.
Then there’s the boring stuff that decides real deployments. Fine-tuning on your own data. Inference inside your VPC so PHI never leaves. A fixed GPU bill instead of a metered one. If you’re running local models on your own hardware, the marginal token is free, and that changes what workloads are even worth building.
How I’m routing it
One model to dream. One model to build. One model to audit.
Frontier for the long agent runs and anything where a wrong answer costs more than the whole month’s token spend. Open weights — K3 for hard reasoning, a smaller open coding model for the volume — for everything else. That’s most of the traffic, and it’s where the 3x price delta compounds.
Three points is not a moat. Three points is a preference.
Price it that way.
That compression kept going. By September the budget tier was close enough that two Intelligence Index points and three cents a task decided it — see Muse Spark 1.3 vs Gemini 3.8 Flash.
#TheAIMogul
Bottom lineThe frontier still wins agentic coding and it still wins the last few points of raw reasoning — but three index points is not a moat you build a company on. Run open weights as the default for volume work and buy frontier tokens only for the jobs that actually break without them.