Solar Mini 4 vs GPT-6 Astra (Medium)

Solar Mini 4 and GPT-6 Astra (Medium) occupy distinctly different positions. Upstage’s model was released on September 22, 2026, while OpenAI’s model arrived earlier, on September 3. The benchmark and operating data point to a clear tradeoff: Solar Mini 4 prioritizes affordability and speed, whereas GPT-6 Astra (Medium) offers a considerably higher capability ceiling.

What the benchmarks show

The intelligence index is the broadest high-level signal in the supplied data. GPT-6 Astra (Medium) scores 49.6, more than double Solar Mini 4’s 24.1. Its coding index is also reported at 76.7, while Solar Mini 4’s coding index is unknown. That makes Astra the better-supported choice for software development and complex general reasoning, although the absence of a Solar coding score is not proof that it cannot code effectively.

On the individual benchmarks, Astra leads on the harder knowledge and science-oriented measures that both models share. Its Humanity’s Last Exam score is 0.527, compared with 0.258 for Solar Mini 4. On SciCode, Astra scores 0.542 versus Solar’s 0.476. GPT-6 Astra also has a GPQA score of 0.939; no corresponding GPQA result is provided for Solar Mini 4, so that comparison should not be treated as a measured loss for Solar.

Solar Mini 4 does produce the stronger result on LCR, scoring 0.833333333333333 against Astra’s 0.796666666666667. This is the clearest benchmark advantage for Upstage’s model and suggests that Solar may be well suited to the particular long-context retrieval or comprehension behavior represented by LCR. It does not outweigh Astra’s broader lead, but it matters for applications whose success resembles that test more closely than general reasoning or coding.

The benchmark picture therefore favors GPT-6 Astra (Medium) for difficult, varied tasks, while Solar Mini 4 retains a meaningful niche where context handling, cost, or response speed is more important than peak benchmark performance.

Speed and cost

The operational difference is substantial. Solar Mini 4 generates 207.104 tokens per second and reaches its first token in 1.125 seconds. GPT-6 Astra (Medium) generates 49.184 tokens per second and has a 4.249-second time to first token. Solar is therefore roughly four times faster in output generation and responds initially more than three seconds sooner. For interactive applications, large-scale classification, or workloads that issue many requests, those differences can affect both user experience and infrastructure capacity.

Pricing reverses the capability comparison in Solar’s favor. Solar Mini 4 costs $0.10 per million input tokens and $0.40 per million output tokens, with a listed blended price of $0.17 per million. GPT-6 Astra (Medium) costs $10.00 per million input tokens and $50.00 per million output tokens, with a $20.00 blended price. On the blended figures, Astra is about 118 times more expensive. The exact bill for a deployment will depend on its input-output mix, but the gap remains large under either direction of usage.

That price difference means Astra’s higher scores need to translate into materially better outcomes before it is economical for routine, high-volume work. Conversely, a more expensive model can still be the better choice when failures, manual review, or difficult engineering tasks carry significant costs.

Which model fits which workflow

Solar Mini 4 fits workflows where request volume and responsiveness dominate. It is a sensible candidate for fast assistants, extraction, summarization, routing, and other production tasks that can tolerate lower general reasoning scores. Its LCR result may also make it attractive for applications centered on retrieving or using information from long inputs. The low token prices make experimentation and broad deployment easier, while its speed supports interactive experiences.

GPT-6 Astra (Medium) fits workflows that require stronger reasoning and coding performance. Its higher intelligence index, leading HLE and SciCode scores, and reported coding index support using it for software generation, technical analysis, research assistance, and other tasks where accuracy on complex problems matters more than latency. Its slower output and higher cost are meaningful constraints, especially if the model is placed behind every routine request rather than reserved for difficult cases.

A hybrid approach is also consistent with the data: Solar Mini 4 can handle inexpensive first-pass work, with Astra reserved for escalation or tasks where deeper reasoning is necessary. That strategy is an architectural option, not a claim about any unreported routing feature.

Decision takeaway

For buyers making a single-model decision, the central question is whether capability or operating efficiency is the binding constraint. GPT-6 Astra (Medium) is the stronger performer across the reported general intelligence, coding, HLE, and SciCode measures, but it is much slower and vastly more expensive. Solar Mini 4 gives up benchmark strength in exchange for exceptional throughput, faster first responses, and low prices, while leading on LCR.

Choose Solar when the workload is large, latency-sensitive, and cost constrained. Choose Astra when difficult reasoning or coding quality has enough business value to justify premium inference. The available data supports that division more strongly than a universal winner.