Blog · 2026-09-06 · Vynaris Team
GPT-6 Astra's Pareto split: 10% fewer output tokens, 75% higher cost per task than Sol
GPT-6 Astra uses 10% fewer output tokens than Sol on the Intelligence Index but costs 75% more per task. On the Coding Agent Index, where Astra cuts tokens by 67%, it breaks even. The workload determines whether token efficiency becomes cost efficiency.
GPT-6 Astra uses about 10% fewer output tokens than GPT-5.6 Sol at max effort on the Artificial Analysis Intelligence Index, but costs 75% more per task. The 2.5x per-token price overwhelms the token savings. On the Coding Agent Index, where Astra cuts tokens by 67%, it breaks even. The workload determines whether token efficiency translates to cost efficiency.
Prices verified 2026-09-06 from OpenAI's pricing page and Artificial Analysis's benchmarking article (published 2026-09-03). Astra launched at $10 input / $50 output per 1M tokens; Sol holds at $4 / $20 promotional Standard.
The two frontiers
Artificial Analysis evaluates models on two flagship indices. GPT-6 Astra tells a different cost story on each.
The Intelligence Index measures single-turn reasoning across 10 evaluations: AA-Briefcase, GDPval-AA v2, Terminal-Bench v2.1, SciCode, Humanity's Last Exam, and others. The Coding Agent Index measures agentic coding performance in real harnesses like Codex and Claude Code, where models iterate over multiple steps.
On the Intelligence Index, Astra scores 61, equal to Sol. On the Coding Agent Index, Astra scores 67, two points above Sol at 65. Same model, same effort level, different benchmarks. The cost implications diverge sharply.
Metric Intelligence Index Coding Agent Index
------------------------------------- ------------------ ------------------
Astra score 61 67
Sol score 61 65
Score difference 0 (equal) +2 (Astra)
Output token reduction (Astra vs Sol) ~10% ~67%
Cost per task vs Sol +75% ~0% (about equal)
Meets 60% break-even? No YesSources: Artificial Analysis benchmarking article, verified 2026-09-06. Token reduction and cost figures are AA's computed values at max effort.
Why 10% fewer tokens does not mean 10% lower cost
The arithmetic is straightforward. Astra's per-token price is 2.5x Sol's across every tier: input, output, cached input, and cache writes all carry the same 2.5x multiple.
Tier Astra (per 1M tok) Sol (per 1M tok) Multiple
------------ ------------------ ---------------- --------
Input $10.00 $4.00 2.5x
Cached input $1.00 $0.40 2.5x
Cache writes $12.50 $5.00 2.5x
Output $50.00 $20.00 2.5xFor Astra to match Sol's cost per task at 2.5x the price, it must consume 40% of Sol's tokens. That is a 60% reduction. Astra's 10% output token reduction on the Intelligence Index covers one-sixth of that gap. The remaining 50 percentage points show up as the 75% cost-per-task premium AA reports.
On a concrete 10,000-output-token task, the numbers are visible:
Sol Astra (full) Astra (10% fewer tokens)
------------- -------- ------------ ------------------------
Output tokens 10,000 10,000 9,000
Cost $0.20 $0.50 $0.45
vs Sol baseline +150% +125%The 10% token reduction saves $0.05. The 2.5x price premium adds $0.30. Net cost increase: $0.25, or 125%. Token efficiency without price efficiency is a partial win.
Where the split inverts: the Coding Agent Index
On the Coding Agent Index, Astra's token efficiency is not 10%. It is 67%. In the Codex harness, Astra uses one third of Sol's output tokens and one third of its agent steps. This is the threshold where the 2.5x sticker collapses.
AA states it directly: at max effort, GPT-6 Astra costs about the same as GPT-5.6 Sol while scoring 2 points higher on the Coding Agent Index. The 67% token reduction overwhelms the 2.5x price premium. Astra also costs less than half of Claude Fable 5 per task for the same score.
The difference between the two indices is the task shape. The Intelligence Index runs single-turn evaluations: one prompt in, one answer out. Token counts are bounded by the question and the expected response length. A 10% reduction in output tokens is the ceiling a model can achieve without changing the answer.
The Coding Agent Index runs multi-step agentic loops: the model plans, writes code, runs tests, reads errors, and iterates. Each step generates reasoning tokens, tool call outputs, and intermediate code. A model that reaches the correct answer in fewer steps compounds its token savings across every step it skips. A 10% per-step reduction applied across 30 steps instead of 1 produces a much larger aggregate saving.
This is the same mechanism we tracked on DeepSWE, where Astra used half of Sol's output tokens and half of its agent steps, bringing per-task cost within $0.06 of Sol despite the 2.5x sticker. The Coding Agent Index result is the broader version of that finding: across AA's coding harness, not just DeepSWE, Astra's multi-step token efficiency meets the break-even.
What changed beyond cost
AA reports two quality improvements that do not show up in the Intelligence Index score but matter for production routing.
First, hallucination rate dropped from 92% to 51% at max effort on AA-Omniscience. Unlike some models, this improvement did not come at the cost of accuracy: Astra increased accuracy by 4 points while cutting hallucinations. For workloads where a wrong answer is worse than no answer, this is a cost-per-outcome improvement that the Intelligence Index does not capture.
Second, Astra gained about 80 Elo points on AA-Briefcase, a long-horizon knowledge work benchmark where models handle multi-week projects with linked tasks and thousands of source files. This is the agentic workload shape where token efficiency compounds. The Intelligence Index's single-turn structure cannot measure this gain.
The tradeoff: Astra lost about 80 Elo points in Presentation Quality on AA-Briefcase, where Sol still leads. If your workload values output polish, Sol retains an edge there.
The buyer threshold
The decision rule is simple. If your workload is single-turn reasoning, question answering, or short-form analysis, Sol at $4/$20 remains cheaper per task. Astra's 10% token reduction cannot overcome the 2.5x price premium. Route to Sol.
If your workload is multi-step coding, agentic tool use, or long-horizon projects, Astra's 67% token reduction meets or exceeds the 60% break-even. Route to Astra. The cost per task is about equal to Sol, and the score is 2 points higher.
The calculator at vynaris.com reproduces per-token cost comparisons for any token shape. But the break-even depends on how many steps your workload runs, which the calculator cannot predict without a workload model.
What this does not tell you
Artificial Analysis is an aggregator, not a primary source. Their Intelligence Index methodology is published, but their cost-per-task figures are modelled from token counts and provider prices, not measured from a production billing system. The 75% cost increase and 10% token reduction are AA's computed values, not independently audited.
The Intelligence Index was updated to v4.2 on 2026-09-04, one day after the benchmarking article was published. The comparison page shows Astra at 55 and Sol at 51 under v4.2, while the article reports both at 61. The score shift reflects the index reweighting, not a model change. We cite the article's "equal at 61" as the primary claim because the Pareto frontier analysis is the article's contribution. The v4.2 numbers do not change the cost conclusion: Astra remains more expensive per task than Sol on the Intelligence Index.
The Coding Agent Index token efficiency figure (one third of Sol's tokens) is specific to the Codex harness. Other coding harnesses may produce different token ratios. The cost break-even depends on the harness, not just the model.
FAQ
Does Astra ever cost less than Sol per task?
On the Intelligence Index, no. The 75% premium holds at max effort. On the Coding Agent Index, Astra costs about the same as Sol, not less. On WANDR, Astra costs less than Fable 5.1 per task, but that comparison is against a different model at the same token price, not against Sol.
If Astra hallucinates less, does that change the cost math?
It changes the cost-per-outcome math, not the cost-per-task math. If your workload penalizes wrong answers heavily, a model that hallucinates 51% of the time versus 92% may be worth 75% more per task. But that depends on your error cost, which is workload-specific. AA's cost-per-task figure does not account for answer quality.
Why does the comparison page show different scores than the article?
The article was published 2026-09-03 using Intelligence Index v4.1.1. The index was updated to v4.2 on 2026-09-04, which reweighted evaluations. The comparison page reflects v4.2. Both versions show Astra near Sol on intelligence, and both show Astra more expensive per task. The Pareto frontier split holds across versions.
Should I switch from Sol to Astra for coding workloads?
The Coding Agent Index data says Astra costs about the same per task while scoring 2 points higher. But AA's cost figures are modelled, not measured from your billing. Run your own eval on your actual workload before switching. The token cost depends on your prompt shapes, context lengths, and retry patterns.