Coding · Reka AI

Reka Flash Reasoning on SWE-bench Verified: score, rank and what it means

Reka Flash Reasoning scores 66.9 on SWE-bench Verified, placing it #172 of 600 models in the index. The benchmark average is 57.5 and the leader is GPT-5.2 Pro at 81.7.

Score
66.9
0–100 scale
Rank
#172
of 600
Percentile
71th
within the index
Benchmark avg
57.5
all models
Leader
81.7
GPT-5.2 Pro
Gap to leader
14.8
points

Score distribution

The chart shows the top 10 models on SWE-bench Verified, with Reka Flash Reasoning highlighted. Longer bars mean higher scores.

GPT-5.2 Pro 81.7
Claude Opus 4.5 Pro 81.1
GPT-5.2 Max 80.5
Claude Sonnet 4.5 Max 80.2
GPT-5.2 80.2
GPT-5.1 Pro 80.1
o4 Max 79.8
Claude Sonnet 4.5 Reasoning 79.6
Claude Opus 4.5 Max 79.5
Claude Opus 4.5 Reasoning 79.5

Top 10 leaderboard

#ModelProviderContextInput / 1MAvg score
1 GPT-5.2 Pro OpenAI 2000000 $21.32 89.6
2 Claude Opus 4.5 Pro Anthropic 200000 $20.98 89.1
3 GPT-5.2 Max OpenAI 1000000 $21.39 89.2
4 Claude Sonnet 4.5 Max Anthropic 1000000 $19.90 87.2
5 GPT-5.2 OpenAI 2000000 $19.71 88.0
6 GPT-5.1 Pro OpenAI 200000 $19.77 87.8
7 o4 Max OpenAI 2000000 $19.51 86.2
8 Claude Sonnet 4.5 Reasoning Anthropic 400000 $19.48 87.7
9 Claude Opus 4.5 Max Anthropic 1000000 $20.90 87.8
10 Claude Opus 4.5 Reasoning Anthropic 2000000 $20.91 88.2

What this score means

On SWE-bench Verified, Reka Flash Reasoning sits at #172 of 600. The leader, GPT-5.2 Pro, scores 81.7, so the gap to the top is 14.8 points. The median model in this index scores 61.0, which puts Reka Flash Reasoning above the midpoint.

Reka AI models average 61.8 on this benchmark. Reka Flash Reasoning is 5.1 points above that provider line, so it is a stronger result than the average Reka AI model.

Among multimodal models, the average is 66.4. Reka Flash Reasoning is 0.5 points above that group. This is the more useful comparison if you are shortlisting within the same tier rather than across the whole index.

Read a 66.9 as a relative signal, not an absolute guarantee. Benchmark scores depend on prompt format, evaluation harness and contamination controls. Use this page to narrow the shortlist, then test the model on your own data.

Price context

At $10.70 per million input tokens, Reka Flash Reasoning is above the median input price of $4.00 across the 600 models scored on SWE-bench Verified. The models immediately around it in the ranking are shown below so you can compare score and price together.

ModelProviderContextInput / 1MAvg score
Claude Haiku 4.5 Vision Anthropic 128000 $13.01 79.2
Reka Core Turbo Reka AI 32768 $12.58 78.4
ERNIE 4.0 Baidu 32768 $11.37 76.7
Llama 3.3 Groq Chat Groq 32768 $4.06 76.8
Reka Flash Reasoning Reka AI 32768 $10.70 75.6
Reka Core Pro Reka AI 65536 $13.44 79.9
Reka Core Reasoning Reka AI 32768 $13.11 78.5

Other benchmarks for Reka Flash Reasoning

BenchmarkScoreRankPercentileRelative
MMLU-Pro 77.8 251 58th
GPQA Diamond 70.4 253 58th
HumanEval 81.5 199 67th
MATH-500 77.0 281 53th
AIME 2025 73.9 186 69th
LiveCodeBench 66.4 267 56th
IFEval 85.2 187 69th
TruthfulQA 76.3 301 50th
HellaSwag 84.2 273 55th
MMMU 72.2 237 61th
Arena Elo 1392.9 236 61th

FAQ

What score does Reka Flash Reasoning get on SWE-bench Verified?

Reka Flash Reasoning scores 66.9 on SWE-bench Verified in this index. The benchmark average is 57.5 and the top score is 81.7.

How does Reka Flash Reasoning rank on SWE-bench Verified?

It ranks #172 of 600 models, in the 71st percentile. The leader is GPT-5.2 Pro at 81.7.

Is Reka Flash Reasoning good at SWE-bench Verified?

Yes, relative to the index: it is above the median and inside the top half of scores. The result should be read alongside its price and context window.

How is the SWE-bench Verified score calculated?

SWE-bench Verified is a coding benchmark. Scores in this index are modelled estimates on a 0–100 scale (or Elo for Arena), derived from capability tier and benchmark difficulty. They are not official leaderboard results.

What does Reka Flash Reasoning cost?

Reka Flash Reasoning costs $10.70 per million input tokens and $57.45 per million output tokens.

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