Dola Seed 2.0 Pro vs Claude Fable 5
Which one, when — curated verdict, not a benchmark table
Dola-Seed-2.0-pro is the cheaper of the two by a wide margin — 20x less per input token ($0.5 vs $10) and about 16.7x less per output token ($3 vs $50) — and it is the only one that accepts video input, allows up to 131072 output tokens, and lets you turn thinking off when you want a fast plain answer. claude-fable-5 costs more but carries a 1000000-token context, roughly 3.9x the 256000 of Dola-Seed-2.0-pro, with thinking always on. Pick claude-fable-5 for very long single-shot documents; pick Dola-Seed-2.0-pro for video inputs, cost control, or high-volume work.
Pricing
| Dola Seed 2.0 Pro | Claude Fable 5 | Δ | |
|---|---|---|---|
| Input / 1M tokens | $0.5 | $10 | 0.05× |
| Output / 1M tokens | $3 | $50 | 0.06× |
| Cache read / 1M tokens | $0.1 | $1 | 0.1× |
| Cache write | — | 1.25x (5m) / 2x (1h) | — |
Rates from the live catalog at build time; each model page carries the current card.
Where they sit — input price per 1M tokens across all 63 chat models on this billing unit (log scale)
Capabilities
| Dola Seed 2.0 Pro | Claude Fable 5 | |
|---|---|---|
| Tool use | yes | yes |
| Thinking control | configurable | always on |
| Structured output | — | yes |
| Prompt caching | implicit + explicit | explicit (you mark the prefix) |
| Cache lifetime | not published | 5m default, 1h option |
| Minimum cached prefix | 1024 tokens | 1024 tokens |
Specs
| Dola Seed 2.0 Pro | Claude Fable 5 | |
|---|---|---|
| Input modalities | text image video | text image |
| Output modalities | text | text |
| Released | 2026-02-14 | 2026-06-09 |
| Knowledge cutoff | — | 2026-01 |
| Context window | 256K | 1M |
| Max output | 131K | 128K |
| Thinking parameter |
| output_config.effort (thinking.type is adaptive-only and needs no configuration) |
| Accepted values | thinking.type
reasoning_effort
| effort
both "enabled" and "disabled" return 400 |
| Default | enabled, with reasoning_effort medium and thinking summary on | thinking always on (adaptive) effort
|
Specs are transcribed from each vendor’s documentation; a row a vendor does not publish is left out rather than inferred. Full sources: Dola Seed 2.0 Pro · Claude Fable 5
One prompt, both models — measured through the gateway
Dola Seed 2.0 Pro passed · 3 sentences
Reasoning-enabled LLMs do not generate final answers in one pass: they run hundreds of unshown internal trial, cross-check, and correction steps for every user request, and every one of these hidden steps consumes billable GPU time just like the visible output text. Unlike our prior standard chat workload which only processed exactly what users typed, reasoning mode automatically executes these validation loops by default, with no visible indicator to end users that extra compute is being consumed. This 2x increase is not a billing error or rogue usage spike: industry benchmarks consistently put reasoning workloads at 1.7-2.6x the compute cost of equivalent standard LLM requests, and this pricing dynamic is almost never clearly disclosed on vendor rate cards.
out 563 tok (+416 thinking) latency 9.4 s
Claude Fable 5 passed · 3 sentences
When we enabled reasoning, the model began generating extensive internal "thinking" tokens before each answer, and those tokens are billed as output even though users never see them. Output tokens typically cost several times more than input tokens, so adding thousands of hidden reasoning tokens per request effectively multiplied our per-query cost. In short, we're paying for a much longer response behind the scenes in exchange for higher answer quality, and we can manage this by tuning reasoning effort levels or reserving reasoning mode for complex tasks only.
out 228 tok latency 6.7 s
Instruction following (exactly three sentences — countable), audience shaping (CFO register), and the hidden-thinking billing gap the token meters below expose.
Dola Seed 2.0 Pro passed · 8/8 cases
### Corrected Implementation ```python def longest_run(xs): if not xs: return 0 best = 1 cur = 1 for i in range(1, len(xs)): if xs[i] == xs[i - 1]: cur += 1 best = max(best, cur) else: cur = 1 return best ``` --- ### Bug Explanation The original code has three critical linked flaws: 1. It initialized run counters `best` and `cur` to `0` instead of `1`, failing to account for the fact that *every individual element is already a valid run of length 1* -- this causes single-element inputs to incorrectly return `0`. 2. `cur` only counted how many extra matching elements followed the start of a run, so it always tracked one less than the actual run length, and it reset to `0` (instead of `1`) when a new run began, preserving this off-by-one error for all runs. 3. […]
out 1278 tok (+951 thinking) latency 16.7 s
Claude Fable 5 passed · 8/8 cases
## Fixed code ```python def longest_run(xs): if not xs: return 0 best = cur = 1 for i in range(1, len(xs)): if xs[i] == xs[i - 1]: cur += 1 else: cur = 1 best = max(best, cur) return best ``` ## Explanation The original function counts **adjacent equal pairs** rather than **items in a run**: a run of length *n* contains only *n − 1* equal neighbor pairs, so every result is off by one — a single-element list yields 0 instead of 1, and `[1, 2, 2]` yields 1 instead of 2 (which callers perceive as "missing" the run at the end). The fix is to initialize both `cur` and `best` to 1 (any nonempty list has a run of at least length 1), reset `cur` to 1 rather than 0 when
out 531 tok latency 12.5 s
Whether the fix is actually correct (runnable), explanation density, and token efficiency on a bounded task.
Dola Seed 2.0 Pro passed · 5/5 fields, guidance null
{ "company": "Meridian Robotics", "quarter": "September quarter", "revenue_usd_m": 412, "growth_pct": 11, "guidance": null }
out 506 tok (+452 thinking) latency 8.6 s
Claude Fable 5 passed · 5/5 fields, guidance null
```json { "company": "Meridian Robotics", "quarter": "Q3 (September quarter)", "revenue_usd_m": 412, "growth_pct": 11, "guidance": null } ``` Notes on interpretation: "up eleven and change" is rendered as 11 (the precise figure isn't stated), and guidance is null because it was explicitly withheld.
out 192 tok latency 6.5 s
Schema obedience (no invented fields), hallucination pressure (guidance is explicitly withheld), and structured-output path differences.
Dola Seed 2.0 Pro passed · 123 words, 0 banned, 1 question
Today we’re rolling out cross-provider prompt caching for our API gateway. For engineering teams routing LLM requests across OpenAI, Anthropic, Mistral and open source models, this feature stores identical prompt payloads at the gateway layer, rather than relying on per-provider cache implementations limited to single endpoints. How much time and compute could your team save by avoiding redundant token processing for repeated system prompts, context windows, or common user queries? Cache hits return responses in under 10ms, with configurable TTL, granular purge controls, and per-application cache partitioning. Early access teams running support bots, batch inference and internal assistants recorded 42-67% lower LLM spend. This feature is live for all gateway users today, with no required code changes to existing routing workflows. (120 words)
out 1041 tok (+872 thinking) latency 11.4 s
Claude Fable 5 passed · 120 words, 0 banned, 1 question
**Introducing Cross-Provider Prompt Caching for our API Gateway** Starting today, your repeated prompts are cached once and reused across OpenAI, Anthropic, Google, and any other provider you route through the gateway. Instead of paying full token costs every time your application sends the same system prompt or context block, the gateway detects matching prefixes and serves cached results in milliseconds. Why keep spending money and latency on identical requests? Early adopters report cost reductions of up to 60 percent on high-volume workloads, along with faster median response times. Configuration is simple: enable caching in your dashboard, set a TTL, and choose which routes participate. Cache entries are encrypted at rest and never shared between accounts. Available now on all plans.
out 1173 tok latency 18.2 s
Constraint obedience (word budget, banned-word list, the single question), style fingerprint, and length control.
Switch between them with one line
Both ids are in every tab below — the highlighted pair of lines is the only edit. Same endpoint, same key, same request shape.
from openai import OpenAI
client = OpenAI(
base_url="https://synthorai.io/v1",
api_key="sk-syn-...",
)
resp = client.chat.completions.create(
model="Dola-Seed-2.0-pro",
# model="claude-fable-5", # uncomment this line, comment the one above
messages=[{"role": "user", "content": "Summarize this diff"}],
reasoning_effort="medium",
)
print(resp.choices[0].message.content)import OpenAI from "openai";
const client = new OpenAI({
baseURL: "https://synthorai.io/v1",
apiKey: "sk-syn-...",
});
const resp = await client.chat.completions.create({
model: "Dola-Seed-2.0-pro",
// model: "claude-fable-5", // uncomment this line, comment the one above
messages: [{ role: "user", content: "Summarize this diff" }],
reasoning_effort: "medium",
});
console.log(resp.choices[0].message.content);curl https://synthorai.io/v1/chat/completions \
-H "Authorization: Bearer sk-syn-..." \
-H "Content-Type: application/json" \
-d '{
"model": "Dola-Seed-2.0-pro",
# "model": "claude-fable-5", # uncomment this line, comment the one above
"messages": [{"role": "user", "content": "Hello"}],
"reasoning_effort": "medium"
}'package main
import (
"context"
"fmt"
"github.com/openai/openai-go/v3"
"github.com/openai/openai-go/v3/option"
)
func main() {
client := openai.NewClient(
option.WithBaseURL("https://synthorai.io/v1"),
option.WithAPIKey("sk-syn-..."),
)
resp, _ := client.Chat.Completions.New(context.TODO(), openai.ChatCompletionNewParams{
Model: "Dola-Seed-2.0-pro",
// Model: "claude-fable-5", // uncomment this line, comment the one above
Messages: []openai.ChatCompletionMessageParamUnion{
openai.UserMessage("Summarize this diff"),
},
ReasoningEffort: openai.ReasoningEffortMedium,
})
fmt.Println(resp.Choices[0].Message.Content)
}import com.openai.client.OpenAIClient;
import com.openai.client.okhttp.OpenAIOkHttpClient;
import com.openai.models.chat.completions.*;
import com.openai.models.ReasoningEffort;
OpenAIClient client = OpenAIOkHttpClient.builder()
.baseUrl("https://synthorai.io/v1")
.apiKey("sk-syn-...")
.build();
ChatCompletion resp = client.chat().completions().create(
ChatCompletionCreateParams.builder()
.model("Dola-Seed-2.0-pro")
// .model("claude-fable-5") // uncomment this line, comment the one above
.addUserMessage("Summarize this diff")
.reasoningEffort(ReasoningEffort.MEDIUM)
.build());
System.out.println(resp.choices().get(0).message().content().orElse(""));FAQ
Which is cheaper, Dola Seed 2.0 Pro or Claude Fable 5?
Dola Seed 2.0 Pro is cheaper on input / 1m tokens ($0.5 vs $10, 20× apart). Other rows may point the other way — the table above carries the full card, and real cost depends on your mix.
Can I A/B test Dola Seed 2.0 Pro against Claude Fable 5 without two integrations?
Yes. Both are served through the same OpenAI-compatible endpoint with one API key — switching is a one-line model-string change, so you can route a fraction of traffic to each and compare bills directly.
Do Dola Seed 2.0 Pro and Claude Fable 5 support prompt caching?
Yes — both bill cached reads below their input rate, so warm-prefix workloads cost less than the list rates suggest. The exact cache-read rows are in the pricing table above.