As of August 20, 2026, Solar Pro 4 is 44% cheaper per million tokens than Qwen3.5-27B. Qwen3.5-27B is $0.30 / $2.40 per million input/output tokens versus Solar Pro 4 at $0.30 / $1.20. Sources: https://bailian.console.aliyun.com/ and https://www.upstage.ai
Sources: Qwen pricing (https://bailian.console.aliyun.com/); Upstage pricing (https://www.upstage.ai)
Qwen3.5-27B
Qwen
Input
$0.30
Output
$2.40
Solar Pro 4
Upstage
Input
$0.30
Output
$1.20
Token pricing, specs, and benchmarks side by side
Qwen3.5-27BSolar Pro 4Verdict
Solar Pro 4 offers both lower pricing and stronger benchmark performance across the board, making it the clear value leader in this comparison.
As of August 20, 2026, Solar Pro 4 is 44% cheaper on blended LLM API pricing. Solar Pro 4 costs $0.30 per million input tokens and $1.20 per million output tokens ($1.50 blended 1M-in + 1M-out). Qwen3.5-27B costs $0.30 / $2.40 per million tokens ($2.70 blended). Sources: https://bailian.console.aliyun.com/ and https://www.upstage.ai. The cheapest LLM for your app still depends on how many output tokens you generate.
Qwen3.5-27B Qwen API pricing is $0.30 input and $2.40 output per million tokens via Qwen. Solar Pro 4 Upstage API pricing is $0.30 input and $1.20 output per million tokens via Upstage. Use those four numbers, not ChatGPT Plus or Claude Pro subscription prices, when you are comparing APIs.
Qwen3.5-27B leads on SWE-bench Verified: Qwen3.5-27B at 72.4% vs Solar Pro 4 at 70.6%. SWE-bench measures real GitHub fixes; LiveCodeBench measures contest programming.
Qwen3.5-27B supports a 262K token context window, while Solar Pro 4 supports 524K tokens. Solar Pro 4 offers a larger context window. Filling a larger window bills more input tokens, so the cheaper-per-million model can still cost more on long documents.
Solar Pro 4 leads on 4 of 6 shared benchmarks versus Qwen3.5-27B's 2 wins. Check the comparison table for GPQA Diamond, SWE-bench, MMLU, HLE, and the other evals we track. Token price and benchmark score together are the usual LLM comparison, not either number alone.
Both Qwen3.5-27B and Solar Pro 4 are production API models. For cost-sensitive production traffic, Solar Pro 4 has better economics on the blended 1M-in + 1M-out scale. For maximum capability, weight the benchmark table for your domain. Many production stacks route cheap models for drafts and a frontier model for the hard turn, which is usually cheaper than sending everything to the expensive API.
Yes. Call Qwen3.5-27B through Qwen and Solar Pro 4 through Upstage with separate API keys, or through a gateway that already wraps both. Keep prompts in tokens, not characters, when you estimate the invoice. AnotherWrapper templates can swap providers without rewriting auth and billing.
List rates are the latest published Qwen API pricing and Upstage API pricing, quoted per million tokens. Official rate sheets: https://bailian.console.aliyun.com/ and https://www.upstage.ai. Prompt caching, batch APIs, and committed-use discounts can change the invoice. Benchmark scores are from official publications and independent evals, updated as new numbers land.
Qwen3.5-27B supports up to 66K output tokens per request, while Solar Pro 4 supports up to 131K output tokens. Output tokens are usually the expensive half of API pricing, so a higher max output cap is a capability, not a discount.
Qwen3.5-27B has a measured throughput of approximately 6.735706278284246 tokens/second. Throughput data for Solar Pro 4 is not yet available. Throughput (tokens per second) and time-to-first-token are why two models with the same per-million-token price can still feel different in chat and agents.
Use the model pickers at the top of this LLM comparison. Search any model in the index and the URL updates. Open Qwen3.5-27B or Solar Pro 4 from the sidebar for the single-model API pricing page, or go back to the LLM pricing table to sort by cheapest blended cost.
This comparison covers API pricing, context window, throughput, and benchmark scores. Qwen3.5-27B costs $0.30/M input and $2.40/M output. Solar Pro 4 costs $0.30/M input and $1.20/M output.
Rank both on the LLM leaderboard, SWE-bench, and Humanity's Last Exam.
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