Models

119 models, one API key. Compare them by what they do and what they cost.

CompareModelPrecisionCapabilities
1M$2.70$13.50$0.2739tok/s3.34s
100%
1M$0.2025$0.81$0.0202575tok/s1.05s
100%
1M$1.688$5.738—160tok/s1.33s
100%
1M$0.081$0.4725$0.0135123tok/s0.90s
100%
1M$2.70
$5.40
$13.50
$27.00
$0.13538tok/sfrom traffic, 7d8.50sfrom traffic, 7d
96.3%
1M$5.40$27.00—
1M$1.35$3.645$0.135132tok/s1.06s
100%
1M$0.135$0.675$0.013553tok/s0.84s
100%
1M$2.70$13.50$0.13536tok/s1.34s
100%
1M$2.70$13.50$0.13546tok/s0.86s
100%
1M$5.40$27.00$0.2727tok/s1.32s
100%
1M$0.1639$0.8195$0.013545tok/s1.40s
100%
500K$2.16$6.48$0.54
1M$0.5805$1.1745—69tok/s0.75s
100%
1M$0.189$0.378—136tok/s0.46s
100%
32K$0.0567$0.00—
1M$0.1323
$0.27
$0.3969
$0.81
—41tok/s1.11s
100%
—$0.019 / image12s/img11.60s
—$0.108 / song
1M$13.50$67.50$1.35
1M$1.013
$2.026
$5.063
$10.126
—109tok/sfrom traffic, 7d18.18sfrom traffic, 7d
95.5%
1M$1.688$5.738—
1M$13.50$67.50—
1M$1.1259$3.3764—43tok/s8.70s
100%
—$0.006075 / min1.55sfrom traffic, 7d
100%
—$0.00675 / min
81.0%
1M$0.2045$0.641—27tok/sfrom traffic, 7d2.29sfrom traffic, 7d
77.2%
1M$0.172$0.5733—61tok/sfrom traffic, 7d3.01sfrom traffic, 7d
99.9%
1M$0.3892$1.168—
98.4%
1M$1.178$3.702—
1M$0.3826$2.732—
98.1%
1M$1.013
$2.026
$5.063
$10.126
$0.05062532tok/sfrom traffic, 7d4.47sfrom traffic, 7d
84.3%
500K$2.758$8.274—
131K$0.4554$1.821—
100%
1M$1.773$6.025—
1M$2.228$6.684$0.2781
1M$0.1859$0.3718—
1M$0.0435$0.1886$0.008190tok/sfrom traffic, 7d31.76sfrom traffic, 7d
99.8%
1M$6.75$33.75$0.675
1M$1.013$5.063$0.10125
87.5%
1M$0.405$3.375$0.040548tok/s1.29s
99.6%
1M$1.542$5.243—
1M$3.052$15.258$0.40524tok/sfrom traffic, 7d2.05sfrom traffic, 7d
100%
1M$2.70
$5.40
$13.50
$27.00
$0.3375
1M$3.151
$5.40
$15.753
$27.00
$0.39387531tok/sfrom traffic, 7d5.21sfrom traffic, 7d
98.1%
1M$2.70$16.20$0.27
1M$2.70$16.20$0.27
1M$0.2703$1.621$0.027
100%
1M$0.2944$1.767$0.02769tok/sfrom traffic, 7d7.54sfrom traffic, 7d
95.3%
500K$2.73$8.189—
262K$0.1594$0.6576—
100%
1M$2.70$13.50$0.2741tok/sfrom traffic, 7d4.30sfrom traffic, 7d
100%
1M$0.9029$3.083$0.29835fp4 · fp862tok/s4.08s
100%
262K$1.009$4.774—
1M$13.50$67.50—
1M$0.675$3.375$0.135fp4 · fp8
1M$0.4325$1.731$0.0864
100%
1M$0.3956$1.582$0.0756fp4 · fp8
100%
256K$0.2229$1.282$0.054fp8
100%
1M$2.071$6.207$0.16875
256K$1.389$2.777$0.27
1M$2.025$12.15$0.2025
95.8%
1M$1.92$3.838$0.27
1M$0.123$0.2458$0.024320tok/s2.06s
99.9%
—$0.072 / image36s/img35.93s
97.7%
262K$0.5909$2.758—28tok/s1.64s
100%
1M$6.75$33.75$0.675
203K$1.89$5.94$0.27675fp4 · fp8
1M$0.4388$2.633—51tok/s9.78s
100%
128K$0.1642$0.4689$0.1642fp4 · fp89tok/s7.02s
99.7%
2M$1.767$3.534—
2M$1.763$3.528—
205K$0.405$1.62—
100%
—$0.061 / image10s/img10.40s
1M$2.70$16.20$0.27
—$0.108 / image
1M$4.05$20.25$0.405
100%
—$0.054 / image21s/img21.30s
—$0.3375 / image
197K$0.4188$1.675—
98.4%
—$0.1512 / sec
203K$0.081$0.54$0.0135fp8
100%
1M$0.675$4.05$0.067542tok/s1.44s
90.8%
160K$0.518$0.7571—fp4
—$0.15525 / sec
—$0.0405 / image
—$0.135 / sec
200K$1.35$6.75$0.135172tok/sfrom traffic, 7d4.85sfrom traffic, 7d
100%
2K$0.0027$0.00—
128K$0.0473$0.2303—
100%
131K$0.1797$1.175$0.03375fp8
100%
131K$0.3546$1.612$0.135fp4 · fp847tok/s1.17s
96.2%
33K$0.0135$0.00—
100%
33K$0.0135$0.00—
33K$0.027$0.00—
41K$0.135$0.00—
—$0.054 / image
33K$0.108$0.378—
97.2%
1M$0.3344$1.337—fp8
100%
—$0.07 / 1K char
—$0.04 / 1K char
—$0.081 / image17s/img16.68s
200K$4.05$20.25—4tok/sfrom traffic, 7d6.28sfrom traffic, 7d
127K$1.35$1.35—18tok/s2.10s
100%
—$0.005 / image16s/img16.24s
100%
64K$0.7425$2.957—55tok/s1.60s
100%
128K$0.135$0.432—13tok/s1.10s
100%
—$0.2025 / 1K char
—$0.0009 / min0.28sfrom traffic, 7d
99.3%
—$0.2025 / 1K char
—$0.00405 / min0.69sfrom traffic, 7d
99.6%
1K$0.0135$0.00—
8K$0.0135$0.00—
1K$0.0135$0.00—
1K$0.00675$0.00—
—$0.405 / 1K char1.54sfrom traffic, 7d
100%
1K$0.00675$0.00—
1K$0.00675$0.00—
1K$0.00675$0.00—

Token models priced per 1M tokens. The cached column is what a repeated prompt prefix costs on that model — a rate of its own, read from the same source as the input rate beside it, not a fixed fraction of it. It runs from a tenth of the input rate to the full input rate depending on the model, and a dash means nobody publishes one, which is a different statement from “free”. Image, video, and audio models priced per unit (image, second, video, minute, song, call, or 1K characters). Throughput and latency are a probe with an identical input when Kyma has that sample. If a row is labeled “from traffic, 7d”, that number is production traffic instead — real prompts, not comparable across models. Image throughput is seconds per succeeded job (30-day median). Video, music and realtime stay empty until a job exists. An empty throughput, latency or uptime cell means Kyma does not yet have a number for that cell. Uptime is a percentage only after 20 observations, over 30 days, probes and real requests together, including Kyma's own accounts. Rankings demand is the same total. Precision names the numeric formats a model may be served at that are BELOW the one its creator released it in, and it is filled on the 14 models where that is true. A low format is not a downgrade when it is how the model shipped, so this is a comparison against the creator’s own model card rather than a list of small numbers. An empty precision cell means there is nothing to flag: either no route reports going under the release, or nobody could source what the release was. Those two are different answers and the model’s own page prints which one it is.

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