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GPT-6.1 Terra vs Palmyra X5

Published LiveBench scores across all seven categories, live list pricing, context windows, and the measured cost of a point of capability — for both models, side by side.

OpenRouter + LiveBenchAll comparisonsFull leaderboard

GPT-6.1 Terra and Palmyra X5 are priced within ~10% of each other.

Palmyra X5 does not have a published LiveBench run, so this comparison covers price, context and declared capabilities only. A missing score means "not evaluated", not "worse" — the right way to separate these two is an eval on your own workload.

openai

GPT-6.1 Terra

Blended / 1M
$2.00
Context
1.1M
Released
Oct 26, 2026
Overall score
77.8
reasoningtool callingfile inputimage inputprompt caching

writer

Palmyra X5

Blended / 1M
$1.95
Context
1.0M
Released
Jan 21, 2026
Overall score
Not evaluated

Specs and pricing

MetricGPT-6.1 TerraPalmyra X5
LiveBench overall

Weighted mean of the seven LiveBench category scores, 0–100. Higher is better.

77.8—
Cost per point

Measured benchmark spend divided by overall score — dollars per point of capability.

$0.0067—
Blended price / 1M

3:1 input:output mix, the usual shape of production traffic.

$2.00$1.95
Input price / 1M$1.00$0.600win
Output price / 1M$5.00win$6.00
Cached input / 1M

Price of an input token served from the prompt cache, where the provider publishes one.

$0.050—
Context window1.1M1.0M
Max output tokens128Kwin8K

Benchmarks by category

An overall score averages away the thing you probably care about. These are the seven LiveBench categories scored separately — GPT-6.1 Terra on top, Palmyra X5 below, both out of 100.

Long-horizon agentic
48.6
—
Coding
94.3
—
Reasoning
88.7
—
Browser
93.1
—
Data analysis
82.3
—
Research
73.9
—
Documents & design
82.3
—

What each one costs to run

Per-token prices are hard to feel. These are monthly list costs for both models across five workload shapes, using each provider's published cached-input rate where there is one.

WorkloadGPT-6.1 TerraPalmyra X5
Support chatbot

1.2K in / 400 out × 200K requests

$571.60/mo$624.00/mo
RAG assistant

8K in / 600 out × 100K requests

$720.00/mo$840.00/mo
Coding agent

40K in / 4K out × 20K requests

$668.00/mo$960.00/mo
Document extraction

20K in / 1.5K out × 50K requests

$1,328/mo$1,050/mo
Bulk classification

500 in / 20 out × 5M requests

$2,525/mo$2,100/mo
Run these two through the cost calculator

GPT-6.1 Terra vs Palmyra X5 FAQ

Which is better, GPT-6.1 Terra or Palmyra X5?

GPT-6.1 Terra and Palmyra X5 are priced within ~10% of each other. Palmyra X5 does not have a published LiveBench run, so this comparison covers price, context and declared capabilities only. A missing score means "not evaluated", not "worse" — the right way to separate these two is an eval on your own workload.

Is GPT-6.1 Terra cheaper than Palmyra X5?

They cost about the same. Both land near $2.00 per million tokens on a 3:1 input:output blend, so price is unlikely to be the deciding factor between them.

Does GPT-6.1 Terra or Palmyra X5 have a bigger context window?

They are effectively the same — 1.1M for GPT-6.1 Terra and 1.0M for Palmyra X5.

Do GPT-6.1 Terra and Palmyra X5 support prompt caching?

GPT-6.1 Terra publishes a cached-input rate of $0.050 per million tokens against a full input rate of $1.00. The catalogue lists no separate cached rate for Palmyra X5, which means the provider does not price it separately here — not that caching is unavailable.

Related comparisons

How these numbers are produced

  • Price — provider list price from OpenRouter, refreshed every 15 minutes. “Blended” is a 3:1 input:output mix.
  • Scores — LiveBench release 2026-10-25, using their own category map. Each model shows its strongest published run. A blank means “not evaluated”, never “bad”.
  • Cost per point — the measured dollars LiveBench spent on the run, divided by the score it earned.
  • “Win” — awarded only past a threshold: one full point on a benchmark score, 10% on a price, 25% on a context window. Anything tighter reports as a tie, because effort settings alone move a LiveBench score by more than that.

Published benchmarks rank models on someone else's tasks. Before committing, see LLM & agent evaluation for building an eval on your own.