Dola Seed 2.0 Lite vs GPT-6.1 Sol
Which one, when
Dola-Seed-2.0-lite is the cheaper and broader-input option: it takes text, image, video and audio, allows thinking to be turned off, and costs 8x less on input ($0.25 vs $2 per million) and 5x less on output ($2 vs $10), with 256000 tokens of context and up to 131072 out. gpt-6.1-sol trades that away for a roughly 4x larger 1050000-token context, reasoning that cannot be turned off, and a 2026-04 knowledge cutoff, but only text and image in. Pick the ByteDance model for audio or video input and high-volume cost control, gpt-6.1-sol for very long text and image contexts.
Benchmarks
GPT-6.1 Sol: the vendor has not published benchmark scores.
Vendor-published: Alibaba (Qwen) Anthropic ByteDance DeepSeek Google MiniMax Moonshot OpenAI Tencent Z.ai
Pricing
| Dola Seed 2.0 Lite | GPT-6.1 Sol | Δ | |
|---|---|---|---|
| Input / 1M tokens | $0.25 | $2 | 0.13× |
| Output / 1M tokens | $2 | $10 | 0.2× |
| Cache read / 1M tokens | $0.05 | $0.1 | 0.5× |
| Cache write | - | no separate charge | - |
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 76 chat models on this billing unit (log scale)
Capabilities
| Dola Seed 2.0 Lite | GPT-6.1 Sol | |
|---|---|---|
| Tool use | yes | yes |
| Thinking control | configurable | always on |
| Structured output | yes | yes |
| Prompt caching | implicit + explicit | implicit (automatic) |
| Cache lifetime | not published | 5-10m, up to 1h |
| Minimum cached prefix | 1024 tokens | 1024 tokens |
Specs
| Dola Seed 2.0 Lite | GPT-6.1 Sol | |
|---|---|---|
| Input modalities | text image audio video | text image |
| Output modalities | text | text |
| Released | 2026-02-14 | 2026-09-29 |
| Knowledge cutoff | - | 2026-04 |
| Context window | 256K | 1.1M |
| Max output | 131K | 128K |
| Thinking parameter |
| reasoning.effort |
| Accepted values | thinking.type
reasoning_effort
| reasoning.effort
|
| Default | enabled, with reasoning_effort medium | 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 Lite · GPT-6.1 Sol
One prompt, both models - measured through the gateway
Dola Seed 2.0 Lite passed · 3 sentences
Enabling reasoning on our production LLM requires the model to run resource-intensive multi-step chain-of-thought inference for every customer-facing query, rather than the single-pass inference that powered our original non-reasoning deployment, and GPU compute—which makes up 90% of our total LLM bill—scales directly with the number of compute cycles each query consumes. Our cloud provider’s LLM billing model charges per million processed tokens, and reasoning workflows generate an average of 2.1x more hidden processing tokens (the intermediate problem-solving steps the model never surfaces to end users) that are still billable, pushing our total monthly token consumption just over double what it was prior to enabling the feature. […]
out 1306 tok (+1096 thinking) latency 18.2 s
GPT-6.1 Sol passed · 3 sentences
Enabling reasoning makes the model do more computational work before answering, and many providers bill the resulting reasoning tokens even though users never see them. That means the same number of requests can cost twice as much because each request consumes more billable tokens, even if the visible answers stay the same length. We should confirm the increase in our usage breakdown and reserve higher reasoning levels for tasks where the improvement justifies the added cost.
out 144 tok (+54 thinking) latency 6.6 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 Lite passed · 8/8 cases
### Corrected Function ```python def longest_run(xs): if not xs: # Handle empty input edge case return 0 best = cur = 1 # Base run length of 1 for any non-empty single element for i in range(1, len(xs)): if xs[i] == xs[i - 1]: cur += 1 best = max(best, cur) else: cur = 1 # Reset run length for the new, different element return best ``` ### Bug Explanation (single paragraph) The original function’s core flaw was that it counted the number of consecutive equal adjacent element pairs, rather than the number of elements in the run itself, leading to all results being off by one, which caused both reported issues: a run of k equal elements only has k-1 consecutive matching pairs, so the original function’s maximum `best` value was always one less than the true element count of the longest run. […]
out 2620 tok (+2217 thinking) latency 33.2 s
GPT-6.1 Sol 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 original function counts equal adjacent pairs rather than items in a run: a run of length `n` has only `n - 1` equal pairs, so a single-element input returns `0`, and a run ending at the last index is also undercounted. It does process the last index, so the issue is not a missing final check. Initializing each nonempty run to `1` and updating `best` after every iteration correctly counts items, including singleton and trailing runs; empty inputs still return `0`.
out 189 tok latency 6.3 s
Whether the fix is actually correct (runnable), explanation density, and token efficiency on a bounded task.
Dola Seed 2.0 Lite passed · 5/5 fields, guidance "Formal guidance withheld pending Q4 clos"
{ "company": "Meridian Robotics", "quarter": "September quarter", "revenue_usd_m": 412, "growth_pct": 11, "guidance": "Formal guidance withheld pending Q4 close" }
out 1502 tok (+1439 thinking) latency 20.2 s
GPT-6.1 Sol passed · 5/5 fields, guidance "Withheld pending the Q4 close"
{ "company": "Meridian Robotics", "quarter": "September quarter", "revenue_usd_m": null, "growth_pct": "eleven and change", "guidance": "Withheld pending the Q4 close" }
out 345 tok (+285 thinking) latency 10.2 s
Schema obedience (no invented fields), hallucination pressure (guidance is explicitly withheld), and structured-output path differences.
Dola Seed 2.0 Lite missed · 141 words, 0 banned, 1 question
Today we launch Cross-Provider Prompt Cache, a new feature for our Core API Gateway built to cut redundant AI costs and latency for teams routing requests across multiple LLM providers. Many development teams rotate between OpenAI, Anthropic, and Google Gemini to balance performance, cost, and capability, but identical prompts get reprocessed from scratch with every provider switch, wasting tokens and slowing response times. This feature stores validated prompt responses at the gateway layer, so repeat requests pull from cache regardless of which provider they route to, with configurable TTLs and built-in compliance with all major provider data policies. […]
out 1870 tok (+1695 thinking) latency 23.1 s
GPT-6.1 Sol passed · 120 words, 0 banned, 1 question
Introducing Cross-Provider Prompt Cache, a new API gateway feature that stores reusable prompts and manages caching across your supported AI providers. Why rebuild the same context every time your application switches models? With one configuration, teams can reuse shared instructions, standardize cache policies, and reduce repeated prompt processing wherever provider caching is available. The gateway handles provider-specific requirements while giving you clear visibility into cache hits, usage, and estimated savings. Set expiration windows, isolate cached content by project, and invalidate entries when prompts change. Your existing routing logic stays intact, so you can compare models without rebuilding your caching workflow. […]
out 588 tok (+435 thinking) latency 13.9 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-lite",
# model="gpt-6.1-sol", # uncomment this line, comment the one above
messages=[{"role": "user", "content": "Summarize this diff"}],
)
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-lite",
// model: "gpt-6.1-sol", // uncomment this line, comment the one above
messages: [{ role: "user", content: "Summarize this diff" }],
});
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-lite",
# "model": "gpt-6.1-sol", # uncomment this line, comment the one above
"messages": [{"role": "user", "content": "Hello"}]
}'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-lite",
// Model: "gpt-6.1-sol", // uncomment this line, comment the one above
Messages: []openai.ChatCompletionMessageParamUnion{
openai.UserMessage("Summarize this diff"),
},
})
fmt.Println(resp.Choices[0].Message.Content)
}import com.openai.client.OpenAIClient;
import com.openai.client.okhttp.OpenAIOkHttpClient;
import com.openai.models.chat.completions.*;
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-lite")
// .model("gpt-6.1-sol") // uncomment this line, comment the one above
.addUserMessage("Summarize this diff")
.build());
System.out.println(resp.choices().get(0).message().content().orElse(""));FAQ
Which is cheaper, Dola Seed 2.0 Lite or GPT-6.1 Sol?
Dola Seed 2.0 Lite is cheaper on input / 1m tokens ($0.25 vs $2, 8.0× 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 Lite against GPT-6.1 Sol 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 Lite and GPT-6.1 Sol 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.