New Sign up free, 10 calls on us. Up to $1, no card needed.

Dola Seed 2.0 Pro vs Claude Haiku 5.5

vs

Which one, when

claude-haiku-5-5 is cheaper and holds more: $0.1 input and $0.5 output against $0.5 and $3 for Dola-Seed-2.0-pro, 5x less on input and 6x less on output, with a 1000000-token context against 256000. Both let you switch thinking off, and both raise their rates on long prompts: claude-haiku-5-5 bills prompts above 100000 tokens at 5x, which brings it level with Dola-Seed-2.0-pro on input and keeps it lower on output, and Dola-Seed-2.0-pro doubles above 128000. Dola-Seed-2.0-pro accepts video input, which claude-haiku-5-5 does not. Pick claude-haiku-5-5 for cost and long inputs, and Dola-Seed-2.0-pro when the input is video.

Pricing

Dola Seed 2.0 Pro Claude Haiku 5.5 Δ
Input / 1M tokens $0.5 $0.1 5×
Output / 1M tokens $3 $0.5 6×
Cache read / 1M tokens $0.1 $0.01 10×
Cache write - 1.25x (5m) / 2x (1h) -

Rates from the live catalogue at build time; each model page carries the current rate card.

Where they sit · input price per 1M tokens across all 78 chat models on this billing unit (log scale)

Capabilities

Dola Seed 2.0 Pro Claude Haiku 5.5
Tool calling yes yes
Thinking control configurable configurable
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 Haiku 5.5
Input modalities text image video text image
Output modalities text text
Released 2026-02-14 2026-10-07
Knowledge cutoff - 2026-06
Context window 256K 1M
Max output 131K 128K
Thinking parameter
  • thinking.type
  • reasoning_effort
output_config.effort
Accepted values
thinking.type
  • enabled
  • disabled (no auto)
reasoning_effort
  • minimal
  • low
  • medium
  • high
output_config.effort
  • low
  • medium
  • high
  • xhigh
  • max
Default enabled, with reasoning_effort medium and thinking summary on medium

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 Haiku 5.5

One prompt, both models, measured through the gateway

PROMPT Explain to a CFO, in exactly three sentences, why our LLM bill doubled after we enabled reasoning. CHECK exactly 3 sentences

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 Haiku 5.5 passed · 3 sentences

Enabling reasoning makes the model generate a long internal chain of thought before its answer, and those thinking tokens are billed as output tokens, which cost several times more than input tokens. The model often produces many times more output per request than it did without reasoning, so the per-call cost rises even when the visible answer looks about the same length. Because that extra spend scales with every request, the bill roughly doubled, and we can control it by setting reasoning budgets, using reasoning only for tasks that need it, or routing simpler requests to a non-reasoning model.

out 332 tok (+159 thinking) latency 2.9 s

Instruction following (exactly three sentences, so countable), audience shaping (CFO register), and the hidden-thinking billing gap the token meters below expose.

PROMPT This function is supposed to return the longest run of consecutive equal items, but callers report it is off by one on single-element inputs and misses runs that end at the last index. Fix it and explain the bug in one paragraph. CHECK fix passes tests

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 Haiku 5.5 passed · 8/8 cases

```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 ``` The bug is that `cur` counts adjacent equal *pairs*, not run *elements*. A run of k equal items produces k−1 matching pairs, so the function returns one less than the true length, and a single-element input produces no pairs at all, so it returns 0 instead of 1. Resetting `cur` to 0 on a mismatch compounds this by starting every run one step behind. The fix starts both `cur` and `best` at 1 (a non-empty list always has a run of at least one element), resets `cur` to 1 on a mismatch, and updates `best` after every element rather than only inside the equal-branch. The empty-list guard keeps the function from returning 1 for `[]`. […]

out 1012 tok (+591 thinking) latency 6.0 s

Whether the fix is actually correct (runnable), explanation density, and token efficiency on a bounded task.

PROMPT Extract a JSON object with fields {company, quarter, revenue_usd_m, growth_pct, guidance} from this text. Use null for anything not stated; add no other fields. CHECK valid JSON, schema exact

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 Haiku 5.5 passed · 5/5 fields, guidance "Formal guidance withheld pending the Q4 "

```json { "company": "Meridian Robotics", "quarter": "September quarter", "revenue_usd_m": null, "growth_pct": 11, "guidance": "Formal guidance withheld pending the Q4 close" } ``` Notes: The text gives revenue as 412 but doesn't state the currency, so I left `revenue_usd_m` as null. Growth is "eleven and change," so 11 is an approximation.

out 725 tok (+572 thinking) latency 4.6 s

Schema obedience (no invented fields), hallucination pressure (guidance is explicitly withheld), and structured-output path differences.

PROMPT Write a 120-word product announcement for an API gateway feature that caches prompts across providers. Forbidden words: "seamless", "unlock", "game-changer", "revolutionize", "empower". Exactly one sentence must be a question. CHECK 120 words, 0 banned words

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 Haiku 5.5 passed · 120 words, 0 banned, 1 question

**Introducing Prompt Cache Sync, the newest feature in GatewayOne.** Teams routing requests across OpenAI, Anthropic, and Google have often paid to process the same long system prompts again and again. Now the gateway stores those prompts once and reuses them wherever they appear, regardless of provider. Repeated context is matched by content hash, so cached prefixes return faster and cost less, with no changes to your application code. How much could this save your team? Enable Prompt Cache Sync from the dashboard, and your next billing report will show the difference. Cache entries follow your existing retention settings and can be purged per project at any time. Availability begins this week for all Scale and Enterprise plans worldwide. Start today.

out 2164 tok (+1929 thinking) latency 9.9 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-haiku-5-5",  # uncomment this line, comment the one above
    messages=[{"role": "user", "content": "Summarize this diff"}],
    reasoning_effort="medium",
)
print(resp.choices[0].message.content)

Get your API key →

FAQ

Which is cheaper, Dola Seed 2.0 Pro or Claude Haiku 5.5?

Claude Haiku 5.5 is cheaper on the "Input / 1M tokens" row ($0.1 vs $0.5, 5.0× apart). Other rows may point the other way; the table above carries the full rate card, and real cost depends on your mix.

Can I A/B test Dola Seed 2.0 Pro against Claude Haiku 5.5 without two integrations?

Yes. Both are served through the same OpenAI-compatible endpoint with one API key. Switching is a one-line change to the model id, so you can route a fraction of traffic to each and compare bills directly.

Do Dola Seed 2.0 Pro and Claude Haiku 5.5 support prompt caching?

Yes. Both bill cache 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.

Related comparisons

From our measured studies